{"id":15181,"date":"2025-11-09T21:27:24","date_gmt":"2025-11-09T20:27:24","guid":{"rendered":"https:\/\/norbert-kathriner.ch\/entity-recognition-and-evidence-weighting-how-llms-decide-who-gets-cited\/"},"modified":"2025-11-13T18:38:14","modified_gmt":"2025-11-13T17:38:14","slug":"entity-recognition-and-evidence-weighting","status":"publish","type":"post","link":"https:\/\/norbert-kathriner.ch\/en\/entity-recognition-and-evidence-weighting\/","title":{"rendered":"Entity Recognition and Evidence Weighting: How LLMs decide who gets cited."},"content":{"rendered":"[vc_row type=&#8221;full_width_content&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overflow=&#8221;visible&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_color=&#8221;#000000&#8243; shape_divider_position=&#8221;top&#8221; shape_divider_height=&#8221;5&#8243; bg_image_animation=&#8221;none&#8221; gradient_type=&#8221;default&#8221; shape_type=&#8221;straight_section&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][vc_raw_html css=&#8221;&#8221;]%3Cscript%20type%3D%22application%2Fld%2Bjson%22%3E%0D%0A%5B%0D%0A%20%20%7B%0D%0A%20%20%20%20%22%40context%22%3A%20%22https%3A%2F%2Fschema.org%22%2C%0D%0A%20%20%20%20%22%40type%22%3A%20%22Organization%22%2C%0D%0A%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Forg%2Fboutique-digitale-kommunikation%22%2C%0D%0A%20%20%20%20%22name%22%3A%20%22Boutique%20f%C3%BCr%20digitale%20Kommunikation%20GmbH%22%2C%0D%0A%20%20%20%20%22url%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2F%22%2C%0D%0A%20%20%20%20%22logo%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fwp-content%2Fuploads%2F2025%2F05%2FFAV-Icon.png%22%2C%0D%0A%20%20%20%20%22foundingDate%22%3A%20%222023-06-01%22%2C%0D%0A%20%20%20%20%22address%22%3A%20%7B%0D%0A%20%20%20%20%20%20%22%40type%22%3A%20%22PostalAddress%22%2C%0D%0A%20%20%20%20%20%20%22streetAddress%22%3A%20%22B%C3%A4chtelenweg%2073%22%2C%0D%0A%20%20%20%20%20%20%22postalCode%22%3A%20%223084%22%2C%0D%0A%20%20%20%20%20%20%22addressLocality%22%3A%20%22Wabern%22%2C%0D%0A%20%20%20%20%20%20%22addressRegion%22%3A%20%22BE%22%2C%0D%0A%20%20%20%20%20%20%22addressCountry%22%3A%20%22CH%22%0D%0A%20%20%20%20%7D%2C%0D%0A%20%20%20%20%22areaServed%22%3A%20%5B%0D%0A%20%20%20%20%20%20%7B%20%22%40type%22%3A%20%22Country%22%2C%20%22name%22%3A%20%22Switzerland%22%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%20%22%40type%22%3A%20%22Country%22%2C%20%22name%22%3A%20%22Germany%22%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%20%22%40type%22%3A%20%22Country%22%2C%20%22name%22%3A%20%22Austria%22%20%7D%0D%0A%20%20%20%20%5D%2C%0D%0A%20%20%20%20%22contactPoint%22%3A%20%5B%0D%0A%20%20%20%20%20%20%7B%0D%0A%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22ContactPoint%22%2C%0D%0A%20%20%20%20%20%20%20%20%22contactType%22%3A%20%22sales%22%2C%0D%0A%20%20%20%20%20%20%20%20%22telephone%22%3A%20%22%2B41-79-929-13-00%22%2C%0D%0A%20%20%20%20%20%20%20%20%22email%22%3A%20%22info%40boutique-digitale-kommunikation.ch%22%2C%0D%0A%20%20%20%20%20%20%20%20%22areaServed%22%3A%20%22DE-AT-CH%22%2C%0D%0A%20%20%20%20%20%20%20%20%22availableLanguage%22%3A%20%5B%20%22de%22%2C%20%22en%22%20%5D%0D%0A%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%5D%2C%0D%0A%20%20%20%20%22identifier%22%3A%20%5B%0D%0A%20%20%20%20%20%20%7B%20%22%40type%22%3A%20%22PropertyValue%22%2C%20%22name%22%3A%20%22UID%20%28CHE%29%22%2C%20%22value%22%3A%20%22CHE-262.781.933%22%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%20%22%40type%22%3A%20%22PropertyValue%22%2C%20%22name%22%3A%20%22Tax%20number%22%2C%20%22value%22%3A%20%22CH-036.4.094.429-7%22%20%7D%0D%0A%20%20%20%20%5D%2C%0D%0A%20%20%20%20%22sameAs%22%3A%20%5B%0D%0A%20%20%20%20%20%20%22https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fboutique-digitale-kommunikation%2F%22%0D%0A%20%20%20%20%5D%0D%0A%20%20%7D%2C%0D%0A%0D%0A%20%20%7B%0D%0A%20%20%20%20%22%40context%22%3A%20%22https%3A%2F%2Fschema.org%22%2C%0D%0A%20%20%20%20%22%40type%22%3A%20%22Person%22%2C%0D%0A%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fperson%2Fnorbert-kathriner%22%2C%0D%0A%20%20%20%20%22name%22%3A%20%22Norbert%20Kathriner%22%2C%0D%0A%20%20%20%20%22jobTitle%22%3A%20%22Founder%20and%20Strategy%20Consultant%22%2C%0D%0A%20%20%20%20%22worksFor%22%3A%20%7B%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Forg%2Fboutique-digitale-kommunikation%22%20%7D%2C%0D%0A%20%20%20%20%22affiliation%22%3A%20%5B%0D%0A%20%20%20%20%20%20%7B%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Forg%2Fboutique-digitale-kommunikation%22%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%0D%0A%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22EducationalOrganization%22%2C%0D%0A%20%20%20%20%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Forg%2Fgraphische-wien%22%2C%0D%0A%20%20%20%20%20%20%20%20%22name%22%3A%20%22Graphische%20Bundes-Lehr-%20und%20Versuchsanstalt%20Wien%22%2C%0D%0A%20%20%20%20%20%20%20%20%22sameAs%22%3A%20%22https%3A%2F%2Fwww.wikidata.org%2Fentity%2FQ1643713%22%0D%0A%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%5D%2C%0D%0A%20%20%20%20%22roleName%22%3A%20%22Teaching%20at%20%27die%20Graphische%27%2C%201995%E2%80%932000%22%2C%0D%0A%20%20%20%20%22sameAs%22%3A%20%5B%0D%0A%20%20%20%20%20%20%22https%3A%2F%2Fwww.linkedin.com%2Fin%2Fnorbert-kathriner-67252725%22%2C%0D%0A%20%20%20%20%20%20%22https%3A%2F%2Fmedium.com%2F%40norbert.kathriner%22%0D%0A%20%20%20%20%5D%0D%0A%20%20%7D%2C%0D%0A%0D%0A%20%20%7B%0D%0A%20%20%20%20%22%40context%22%3A%20%22https%3A%2F%2Fschema.org%22%2C%0D%0A%20%20%20%20%22%40type%22%3A%20%22CreativeWorkSeries%22%2C%0D%0A%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fseries%2Fai-visibility%22%2C%0D%0A%20%20%20%20%22name%22%3A%20%22AI%20Visibility%22%2C%0D%0A%20%20%20%20%22publisher%22%3A%20%7B%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Forg%2Fboutique-digitale-kommunikation%22%20%7D%2C%0D%0A%20%20%20%20%22inLanguage%22%3A%20%5B%20%22en%22%2C%20%22de-CH%22%20%5D%2C%0D%0A%20%20%20%20%22hasPart%22%3A%20%7B%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Flist%2Fai-visibility-cluster%22%20%7D%0D%0A%20%20%7D%2C%0D%0A%0D%0A%20%20%7B%0D%0A%20%20%20%20%22%40context%22%3A%20%22https%3A%2F%2Fschema.org%22%2C%0D%0A%20%20%20%20%22%40type%22%3A%20%22ItemList%22%2C%0D%0A%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Flist%2Fai-visibility-cluster%22%2C%0D%0A%20%20%20%20%22name%22%3A%20%22AI%20Visibility%20%E2%80%93%20Content%20Cluster%20%28DE%2FEN%29%22%2C%0D%0A%20%20%20%20%22itemListElement%22%3A%20%5B%0D%0A%20%20%20%20%20%20%7B%20%22%40type%22%3A%20%22ListItem%22%2C%20%22position%22%3A%201%2C%20%22url%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Farchitekturprinzipien-fuer-ai-visibility%2F%22%2C%20%22name%22%3A%20%22Struktur%20schl%C3%A4gt%20Ranking%3A%20Architekturprinzipien%20f%C3%BCr%20AI%20Visibility%20jenseits%20von%20SEO%22%2C%20%22inLanguage%22%3A%20%22de-CH%22%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%20%22%40type%22%3A%20%22ListItem%22%2C%20%22position%22%3A%202%2C%20%22url%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fen%2Fstructure-beats-ranking-architectural-principles-for-ai-visibility-beyond-seo%2F%22%2C%20%22name%22%3A%20%22Structure%20beats%20ranking%3A%20architectural%20principles%20for%20AI%20visibility%20beyond%20SEO%22%2C%20%22inLanguage%22%3A%20%22en%22%20%7D%2C%0D%0A%0D%0A%20%20%20%20%20%20%7B%20%22%40type%22%3A%20%22ListItem%22%2C%20%22position%22%3A%203%2C%20%22url%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fmit-entitaeten-zu-maschinenlesbaren-strukturen%2F%22%2C%20%22name%22%3A%20%22Mit%20Entit%C3%A4ten%20zu%20maschinenlesbaren%20Strukturen%22%2C%20%22inLanguage%22%3A%20%22de-CH%22%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%20%22%40type%22%3A%20%22ListItem%22%2C%20%22position%22%3A%204%2C%20%22url%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fen%2Fwith-entities-to-machine-readable-structures-for-ai-visibility%2F%22%2C%20%22name%22%3A%20%22With%20entities%20to%20machine-readable%20structures%20for%20AI%20visibility%22%2C%20%22inLanguage%22%3A%20%22en%22%20%7D%2C%0D%0A%0D%0A%20%20%20%20%20%20%7B%20%22%40type%22%3A%20%22ListItem%22%2C%20%22position%22%3A%205%2C%20%22url%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fmaschinenlesbare-kommunikation%2F%22%2C%20%22name%22%3A%20%22Maschinenlesbare%20Kommunikation%22%2C%20%22inLanguage%22%3A%20%22de-CH%22%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%20%22%40type%22%3A%20%22ListItem%22%2C%20%22position%22%3A%206%2C%20%22url%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fen%2Fmachine-readable-or-irrelevant-why-companies-need-to-rethink-now%2F%22%2C%20%22name%22%3A%20%22Machine-readable%20or%20irrelevant%3F%20Why%20companies%20need%20to%20rethink%20now%22%2C%20%22inLanguage%22%3A%20%22en%22%20%7D%2C%0D%0A%0D%0A%20%20%20%20%20%20%7B%20%22%40type%22%3A%20%22ListItem%22%2C%20%22position%22%3A%207%2C%20%22url%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fdie-geo-falle%2F%22%2C%20%22name%22%3A%20%22Die%20GEO-Falle%22%2C%20%22inLanguage%22%3A%20%22de-CH%22%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%20%22%40type%22%3A%20%22ListItem%22%2C%20%22position%22%3A%208%2C%20%22url%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fen%2Fthe-geo-trap%2F%22%2C%20%22name%22%3A%20%22The%20GEO%20Trap%22%2C%20%22inLanguage%22%3A%20%22en%22%20%7D%2C%0D%0A%0D%0A%20%20%20%20%20%20%7B%20%22%40type%22%3A%20%22ListItem%22%2C%20%22position%22%3A%209%2C%20%22url%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fseo-markenfuehrung-zukunft%2F%22%2C%20%22name%22%3A%20%22SEO%20%26%20Markenf%C3%BChrung%3A%20Wer%20f%C3%BChrt%20Marken%20in%20Zukunft%3F%22%2C%20%22inLanguage%22%3A%20%22de-CH%22%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%20%22%40type%22%3A%20%22ListItem%22%2C%20%22position%22%3A%2010%2C%20%22url%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fen%2Fseo-brand-management-who-will-manage-brands-in-the-future-the-role-of-seo-ai%2F%22%2C%20%22name%22%3A%20%22SEO%20%26%20brand%20management%3A%20who%20will%20manage%20brands%20in%20the%20future%3F%22%2C%20%22inLanguage%22%3A%20%22en%22%20%7D%2C%0D%0A%0D%0A%20%20%20%20%20%20%7B%20%22%40type%22%3A%20%22ListItem%22%2C%20%22position%22%3A%2011%2C%20%22url%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fai-gestuetztes-seo%2F%22%2C%20%22name%22%3A%20%22AI-gest%C3%BCtztes%20SEO%22%2C%20%22inLanguage%22%3A%20%22de-CH%22%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%20%22%40type%22%3A%20%22ListItem%22%2C%20%22position%22%3A%2012%2C%20%22url%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fen%2Fai-supported-seo%2F%22%2C%20%22name%22%3A%20%22AI-supported%20SEO%22%2C%20%22inLanguage%22%3A%20%22en%22%20%7D%2C%0D%0A%0D%0A%20%20%20%20%20%20%7B%20%22%40type%22%3A%20%22ListItem%22%2C%20%22position%22%3A%2013%2C%20%22url%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fsichtbar-in-chatgpt%2F%22%2C%20%22name%22%3A%20%22Sichtbar%20in%20ChatGPT%20%E2%80%93%20Leitfaden%22%2C%20%22inLanguage%22%3A%20%22de-CH%22%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%20%22%40type%22%3A%20%22ListItem%22%2C%20%22position%22%3A%2014%2C%20%22url%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fen%2Fbecoming-visible-in-chatgpt-the-guide-to-ai-visibility%2F%22%2C%20%22name%22%3A%20%22Becoming%20visible%20in%20ChatGPT%20%E2%80%93%20The%20guide%20to%20AI%20visibility%22%2C%20%22inLanguage%22%3A%20%22en%22%20%7D%2C%0D%0A%0D%0A%20%20%20%20%20%20%7B%20%22%40type%22%3A%20%22ListItem%22%2C%20%22position%22%3A%2015%2C%20%22url%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fseo-agentur-markenstrategie%2F%22%2C%20%22name%22%3A%20%22Wie%20SEO-Agenturen%20Markenstrategie%20mit%20KI%20optimieren%22%2C%20%22inLanguage%22%3A%20%22de-CH%22%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%20%22%40type%22%3A%20%22ListItem%22%2C%20%22position%22%3A%2016%2C%20%22url%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fen%2Fhow-seo-agencies-optimize-brand-strategy-with-ai%2F%22%2C%20%22name%22%3A%20%22How%20SEO%20agencies%20optimize%20brand%20strategy%20with%20AI%22%2C%20%22inLanguage%22%3A%20%22en%22%20%7D%2C%0D%0A%0D%0A%20%20%20%20%20%20%7B%20%22%40type%22%3A%20%22ListItem%22%2C%20%22position%22%3A%2017%2C%20%22url%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fai-visibility%2F%22%2C%20%22name%22%3A%20%22AI%20Visibility%20%E2%80%93%20die%20neue%20Infrastruktur%20der%20Sichtbarkeit%22%2C%20%22inLanguage%22%3A%20%22de-CH%22%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%20%22%40type%22%3A%20%22ListItem%22%2C%20%22position%22%3A%2018%2C%20%22url%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fen%2Fai-visibility-the-new-infrastructure-for-visibility%2F%22%2C%20%22name%22%3A%20%22AI%20Visibility%20%E2%80%93%20the%20new%20infrastructure%20for%20visibility%22%2C%20%22inLanguage%22%3A%20%22en%22%20%7D%2C%0D%0A%0D%0A%20%20%20%20%20%20%7B%20%22%40type%22%3A%20%22ListItem%22%2C%20%22position%22%3A%2019%2C%20%22url%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fzero-click%2F%22%2C%20%22name%22%3A%20%22Zero-Click-%C3%96konomie%3A%20Sichtbarkeit%20ohne%20Website-Besuch%22%2C%20%22inLanguage%22%3A%20%22de-CH%22%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%20%22%40type%22%3A%20%22ListItem%22%2C%20%22position%22%3A%2020%2C%20%22url%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fen%2Fzero-click-economy-visibility-without-a-website-visit%2F%22%2C%20%22name%22%3A%20%22Zero-click%20economy%3A%20visibility%20without%20a%20website%20visit%22%2C%20%22inLanguage%22%3A%20%22en%22%20%7D%2C%0D%0A%0D%0A%20%20%20%20%20%20%7B%20%22%40type%22%3A%20%22ListItem%22%2C%20%22position%22%3A%2021%2C%20%22url%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fai-visibility-angebot%2F%22%2C%20%22name%22%3A%20%22AI%20Visibility%20%E2%80%93%20Angebot%20%2F%20Audit%22%2C%20%22inLanguage%22%3A%20%22de-CH%22%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%20%22%40type%22%3A%20%22ListItem%22%2C%20%22position%22%3A%2022%2C%20%22url%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fen%2Fai-visibility-offer-have-your-visibility-checked-now%2F%22%2C%20%22name%22%3A%20%22AI%20Visibility%20%E2%80%93%20Offer%20%2F%20Audit%22%2C%20%22inLanguage%22%3A%20%22en%22%20%7D%0D%0A%20%20%20%20%5D%0D%0A%20%20%7D%2C%0D%0A%0D%0A%20%20%7B%0D%0A%20%20%20%20%22%40context%22%3A%20%22https%3A%2F%2Fschema.org%22%2C%0D%0A%20%20%20%20%22%40type%22%3A%20%22DefinedTermSet%22%2C%0D%0A%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Ftermset%2Fai-visibility%22%2C%0D%0A%20%20%20%20%22name%22%3A%20%22AI%20Visibility%20%E2%80%93%20Glossary%22%2C%0D%0A%20%20%20%20%22url%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fen%2Fentity-recognition-and-evidence-weighting%2F%22%2C%0D%0A%20%20%20%20%22hasDefinedTerm%22%3A%20%5B%0D%0A%20%20%20%20%20%20%7B%0D%0A%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22DefinedTerm%22%2C%0D%0A%20%20%20%20%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Fentity-recognition%22%2C%0D%0A%20%20%20%20%20%20%20%20%22name%22%3A%20%22Entity%20Recognition%22%2C%0D%0A%20%20%20%20%20%20%20%20%22description%22%3A%20%22Detecting%20named%20entities%20in%20text%20as%20a%20prerequisite%20for%20disambiguation%20and%20machine-readable%20anchoring.%22%2C%0D%0A%20%20%20%20%20%20%20%20%22sameAs%22%3A%20%22https%3A%2F%2Fwww.wikidata.org%2Fentity%2FQ403574%22%0D%0A%20%20%20%20%20%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%0D%0A%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22DefinedTerm%22%2C%0D%0A%20%20%20%20%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Fentity-linking%22%2C%0D%0A%20%20%20%20%20%20%20%20%22name%22%3A%20%22Entity%20Linking%22%2C%0D%0A%20%20%20%20%20%20%20%20%22description%22%3A%20%22Resolving%20ambiguity%20by%20linking%20mentions%20to%20canonical%20IDs%20%28e.g.%2C%20Wikidata%20Q-IDs%29.%22%2C%0D%0A%20%20%20%20%20%20%20%20%22sameAs%22%3A%20%22https%3A%2F%2Fwww.wikidata.org%2Fentity%2FQ17012245%22%0D%0A%20%20%20%20%20%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%0D%0A%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22DefinedTerm%22%2C%0D%0A%20%20%20%20%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Fcoreference%22%2C%0D%0A%20%20%20%20%20%20%20%20%22name%22%3A%20%22Coreference%20%28incl.%20cross-document%29%22%2C%0D%0A%20%20%20%20%20%20%20%20%22description%22%3A%20%22Resolving%20references%20so%20that%20multiple%20mentions%20of%20the%20same%20entity%20are%20consolidated%20consistently.%22%2C%0D%0A%20%20%20%20%20%20%20%20%22sameAs%22%3A%20%22https%3A%2F%2Fwww.wikidata.org%2Fentity%2FQ63087%22%0D%0A%20%20%20%20%20%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%0D%0A%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22DefinedTerm%22%2C%0D%0A%20%20%20%20%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Finformation-extraction%22%2C%0D%0A%20%20%20%20%20%20%20%20%22name%22%3A%20%22Information%20Extraction%22%2C%0D%0A%20%20%20%20%20%20%20%20%22description%22%3A%20%22Extracting%20fact-bearing%20passages%20as%20a%20precursor%20to%20evidence%20building%20and%20passage%20ranking.%22%2C%0D%0A%20%20%20%20%20%20%20%20%22sameAs%22%3A%20%22https%3A%2F%2Fwww.wikidata.org%2Fentity%2FQ1662562%22%0D%0A%20%20%20%20%20%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%0D%0A%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22DefinedTerm%22%2C%0D%0A%20%20%20%20%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Fopen-information-extraction%22%2C%0D%0A%20%20%20%20%20%20%20%20%22name%22%3A%20%22Open%20Information%20Extraction%20%28OpenIE%29%22%2C%0D%0A%20%20%20%20%20%20%20%20%22description%22%3A%20%22Deriving%20subject%E2%80%93predicate%E2%80%93object%20triples%20directly%20from%20text%20without%20a%20fixed%20ontology.%22%2C%0D%0A%20%20%20%20%20%20%20%20%22sameAs%22%3A%20%22https%3A%2F%2Fwww.wikidata.org%2Fentity%2FQ30692626%22%0D%0A%20%20%20%20%20%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%0D%0A%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22DefinedTerm%22%2C%0D%0A%20%20%20%20%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Fevidence-weighting%22%2C%0D%0A%20%20%20%20%20%20%20%20%22name%22%3A%20%22Evidence%20Weighting%22%2C%0D%0A%20%20%20%20%20%20%20%20%22description%22%3A%20%22Weighing%20evidence%20by%20authority%2C%20recency%2C%20diversity%20and%20ID%20anchors%3B%20formally%20compatible%20with%20Bayesian%20inference.%22%2C%0D%0A%20%20%20%20%20%20%20%20%22sameAs%22%3A%20%5B%0D%0A%20%20%20%20%20%20%20%20%20%20%22https%3A%2F%2Fwww.wikidata.org%2Fentity%2FQ812535%22%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%22https%3A%2F%2Fwww.wikidata.org%2Fentity%2FQ182505%22%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%22https%3A%2F%2Fwww.wikidata.org%2Fentity%2FQ585740%22%0D%0A%20%20%20%20%20%20%20%20%5D%0D%0A%20%20%20%20%20%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%0D%0A%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22DefinedTerm%22%2C%0D%0A%20%20%20%20%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Frag%22%2C%0D%0A%20%20%20%20%20%20%20%20%22name%22%3A%20%22Retrieval-Augmented%20Generation%20%28RAG%29%22%2C%0D%0A%20%20%20%20%20%20%20%20%22description%22%3A%20%22Combining%20LLM%20generation%20with%20live%20retrieval%2Fexternal%20evidence%20to%20ground%20answers.%22%2C%0D%0A%20%20%20%20%20%20%20%20%22sameAs%22%3A%20%22https%3A%2F%2Fwww.wikidata.org%2Fentity%2FQ121362277%22%0D%0A%20%20%20%20%20%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%0D%0A%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22DefinedTerm%22%2C%0D%0A%20%20%20%20%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Fknowledge-graph%22%2C%0D%0A%20%20%20%20%20%20%20%20%22name%22%3A%20%22Knowledge%20Graph%22%2C%0D%0A%20%20%20%20%20%20%20%20%22description%22%3A%20%22A%20graph%20of%20entities%20and%20relations%3B%20basis%20for%20linking%2C%20disambiguation%20and%20evidence%20chains.%22%2C%0D%0A%20%20%20%20%20%20%20%20%22sameAs%22%3A%20%22https%3A%2F%2Fwww.wikidata.org%2Fentity%2FQ33002955%22%0D%0A%20%20%20%20%20%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%0D%0A%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22DefinedTerm%22%2C%0D%0A%20%20%20%20%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Fjson-ld%22%2C%0D%0A%20%20%20%20%20%20%20%20%22name%22%3A%20%22JSON-LD%22%2C%0D%0A%20%20%20%20%20%20%20%20%22description%22%3A%20%22Preferred%20web%20format%20for%20machine-readable%20linked%20data.%22%2C%0D%0A%20%20%20%20%20%20%20%20%22sameAs%22%3A%20%22https%3A%2F%2Fwww.wikidata.org%2Fentity%2FQ6108942%22%0D%0A%20%20%20%20%20%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%0D%0A%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22DefinedTerm%22%2C%0D%0A%20%20%20%20%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Frdfa%22%2C%0D%0A%20%20%20%20%20%20%20%20%22name%22%3A%20%22RDFa%22%2C%0D%0A%20%20%20%20%20%20%20%20%22description%22%3A%20%22Embedding%20RDF%20statements%20directly%20in%20HTML%20attributes.%22%2C%0D%0A%20%20%20%20%20%20%20%20%22sameAs%22%3A%20%22https%3A%2F%2Fwww.wikidata.org%2Fentity%2FQ507673%22%0D%0A%20%20%20%20%20%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%0D%0A%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22DefinedTerm%22%2C%0D%0A%20%20%20%20%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Fmicrodata%22%2C%0D%0A%20%20%20%20%20%20%20%20%22name%22%3A%20%22Microdata%20%28HTML%29%22%2C%0D%0A%20%20%20%20%20%20%20%20%22description%22%3A%20%22Historical%2Fadditional%20markup%20for%20structured%20data%20in%20HTML.%22%2C%0D%0A%20%20%20%20%20%20%20%20%22sameAs%22%3A%20%22https%3A%2F%2Fwww.wikidata.org%2Fentity%2FQ3312185%22%0D%0A%20%20%20%20%20%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%0D%0A%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22DefinedTerm%22%2C%0D%0A%20%20%20%20%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Frdf%22%2C%0D%0A%20%20%20%20%20%20%20%20%22name%22%3A%20%22RDF%22%2C%0D%0A%20%20%20%20%20%20%20%20%22description%22%3A%20%22Resource%20Description%20Framework%3B%20triple%2Fgraph%20data%20model%20for%20semantic%20linking.%22%2C%0D%0A%20%20%20%20%20%20%20%20%22sameAs%22%3A%20%22https%3A%2F%2Fwww.wikidata.org%2Fentity%2FQ54872%22%0D%0A%20%20%20%20%20%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%0D%0A%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22DefinedTerm%22%2C%0D%0A%20%20%20%20%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Fllm%22%2C%0D%0A%20%20%20%20%20%20%20%20%22name%22%3A%20%22Large%20Language%20Model%20%28LLM%29%22%2C%0D%0A%20%20%20%20%20%20%20%20%22description%22%3A%20%22Very%20large%20language%20models%3B%20decide%20visibility%20via%20recognition%2C%20linking%20and%20weighting%20of%20evidence.%22%2C%0D%0A%20%20%20%20%20%20%20%20%22sameAs%22%3A%20%22https%3A%2F%2Fwww.wikidata.org%2Fentity%2FQ115305900%22%0D%0A%20%20%20%20%20%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%0D%0A%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22DefinedTerm%22%2C%0D%0A%20%20%20%20%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Fqid%22%2C%0D%0A%20%20%20%20%20%20%20%20%22name%22%3A%20%22Wikidata%20Q%20Identifier%20%28Q-ID%29%22%2C%0D%0A%20%20%20%20%20%20%20%20%22description%22%3A%20%22Stable%20global%20identifier%20for%20entities%20in%20Wikidata%3B%20primary%20anchor%20for%20disambiguation.%22%2C%0D%0A%20%20%20%20%20%20%20%20%22sameAs%22%3A%20%22https%3A%2F%2Fwww.wikidata.org%2Fentity%2FQ43649390%22%0D%0A%20%20%20%20%20%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%0D%0A%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22DefinedTerm%22%2C%0D%0A%20%20%20%20%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Forcid%22%2C%0D%0A%20%20%20%20%20%20%20%20%22name%22%3A%20%22ORCID%20iD%22%2C%0D%0A%20%20%20%20%20%20%20%20%22description%22%3A%20%22Persistent%20researcher%2Fauthor%20identifier%3B%20useful%20as%20authority%20anchor.%22%2C%0D%0A%20%20%20%20%20%20%20%20%22sameAs%22%3A%20%22https%3A%2F%2Fwww.wikidata.org%2Fentity%2FQ51044%22%0D%0A%20%20%20%20%20%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%0D%0A%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22DefinedTerm%22%2C%0D%0A%20%20%20%20%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Fisin%22%2C%0D%0A%20%20%20%20%20%20%20%20%22name%22%3A%20%22ISIN%22%2C%0D%0A%20%20%20%20%20%20%20%20%22description%22%3A%20%22International%20Securities%20Identification%20Number.%22%2C%0D%0A%20%20%20%20%20%20%20%20%22sameAs%22%3A%20%22https%3A%2F%2Fwww.wikidata.org%2Fentity%2FQ259605%22%0D%0A%20%20%20%20%20%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%0D%0A%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22DefinedTerm%22%2C%0D%0A%20%20%20%20%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Flei%22%2C%0D%0A%20%20%20%20%20%20%20%20%22name%22%3A%20%22Legal%20Entity%20Identifier%20%28LEI%29%22%2C%0D%0A%20%20%20%20%20%20%20%20%22description%22%3A%20%22Global%20identifier%20for%20legal%20entities.%22%2C%0D%0A%20%20%20%20%20%20%20%20%22sameAs%22%3A%20%22https%3A%2F%2Fwww.wikidata.org%2Fentity%2FQ6517388%22%0D%0A%20%20%20%20%20%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%0D%0A%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22DefinedTerm%22%2C%0D%0A%20%20%20%20%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Fprov-o%22%2C%0D%0A%20%20%20%20%20%20%20%20%22name%22%3A%20%22PROV-O%20%2F%20Evidence%20Graph%20%28Provenance%29%22%2C%0D%0A%20%20%20%20%20%20%20%20%22description%22%3A%20%22Model%20for%20provenance%2Fevidence%20chains%3B%20anchor%20for%20evidence-driven%20citation%20paths.%22%2C%0D%0A%20%20%20%20%20%20%20%20%22sameAs%22%3A%20%22https%3A%2F%2Fwww.wikidata.org%2Fentity%2FQ62213429%22%0D%0A%20%20%20%20%20%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%0D%0A%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22DefinedTerm%22%2C%0D%0A%20%20%20%20%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Fnlg%22%2C%0D%0A%20%20%20%20%20%20%20%20%22name%22%3A%20%22Natural%20Language%20Generation%20%28NLG%29%22%2C%0D%0A%20%20%20%20%20%20%20%20%22description%22%3A%20%22Language%20generation%3B%20realization%20step%20after%20planning%2Fweighting%20decisions.%22%2C%0D%0A%20%20%20%20%20%20%20%20%22sameAs%22%3A%20%22https%3A%2F%2Fwww.wikidata.org%2Fentity%2FQ1513879%22%0D%0A%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%5D%0D%0A%20%20%7D%2C%0D%0A%0D%0A%20%20%7B%0D%0A%20%20%20%20%22%40context%22%3A%20%22https%3A%2F%2Fschema.org%22%2C%0D%0A%20%20%20%20%22%40type%22%3A%20%22Article%22%2C%0D%0A%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Farticle%2Fentity-recognition-evidence-weighting-en%22%2C%0D%0A%20%20%20%20%22headline%22%3A%20%22Entity%20Recognition%20and%20Evidence%20Weighting%3A%20How%20LLMs%20decide%20who%20gets%20cited.%22%2C%0D%0A%20%20%20%20%22inLanguage%22%3A%20%22en%22%2C%0D%0A%20%20%20%20%22url%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fen%2Fentity-recognition-and-evidence-weighting%2F%22%2C%0D%0A%20%20%20%20%22image%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fwp-content%2Fuploads%2F2025%2F11%2FEntity-Recognition-und-Evidence-Weighting.jpg%22%2C%0D%0A%20%20%20%20%22datePublished%22%3A%20%222025-11-09%22%2C%0D%0A%20%20%20%20%22dateModified%22%3A%20%222025-11-09%22%2C%0D%0A%20%20%20%20%22author%22%3A%20%7B%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fperson%2Fnorbert-kathriner%22%20%7D%2C%0D%0A%20%20%20%20%22publisher%22%3A%20%7B%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Forg%2Fboutique-digitale-kommunikation%22%20%7D%2C%0D%0A%20%20%20%20%22isPartOf%22%3A%20%7B%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fseries%2Fai-visibility%22%20%7D%2C%0D%0A%20%20%20%20%22mainEntityOfPage%22%3A%20%7B%20%22%40type%22%3A%20%22WebPage%22%2C%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fen%2Fentity-recognition-and-evidence-weighting%2F%22%20%7D%2C%0D%0A%20%20%20%20%22translationOfWork%22%3A%20%7B%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Farticle%2Fentity-recognition-evidence-weighting%22%20%7D%2C%0D%0A%20%20%20%20%22about%22%3A%20%5B%0D%0A%20%20%20%20%20%20%7B%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Fentity-recognition%22%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Fentity-linking%22%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Fcoreference%22%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Finformation-extraction%22%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Fopen-information-extraction%22%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Fevidence-weighting%22%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Frag%22%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Fknowledge-graph%22%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Fjson-ld%22%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Frdfa%22%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Fmicrodata%22%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Frdf%22%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Fllm%22%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Fqid%22%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Fprov-o%22%20%7D%0D%0A%20%20%20%20%5D%2C%0D%0A%20%20%20%20%22keywords%22%3A%20%5B%0D%0A%20%20%20%20%20%20%22AI%20Visibility%22%2C%20%22Entity%20Recognition%22%2C%20%22Entity%20Linking%22%2C%20%22Evidence%20Weighting%22%2C%0D%0A%20%20%20%20%20%20%22RAG%22%2C%20%22Knowledge%20Graph%22%2C%20%22JSON-LD%22%2C%20%22RDFa%22%2C%20%22Microdata%22%2C%20%22QID%22%0D%0A%20%20%20%20%5D%2C%0D%0A%20%20%20%20%22citation%22%3A%20%5B%0D%0A%20%20%20%20%20%20%7B%0D%0A%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22ScholarlyArticle%22%2C%0D%0A%20%20%20%20%20%20%20%20%22name%22%3A%20%22Retrieval-Augmented%20Generation%20for%20Knowledge-Intensive%20NLP%20Tasks%22%2C%0D%0A%20%20%20%20%20%20%20%20%22sameAs%22%3A%20%22https%3A%2F%2Farxiv.org%2Fabs%2F2005.11401%22%0D%0A%20%20%20%20%20%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%0D%0A%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22ScholarlyArticle%22%2C%0D%0A%20%20%20%20%20%20%20%20%22name%22%3A%20%22A%20Comprehensive%20Survey%20of%20Retrieval-Augmented%20Generation%20%28RAG%29%22%2C%0D%0A%20%20%20%20%20%20%20%20%22sameAs%22%3A%20%22https%3A%2F%2Farxiv.org%2Fabs%2F2410.12837%22%0D%0A%20%20%20%20%20%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%0D%0A%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22ScholarlyArticle%22%2C%0D%0A%20%20%20%20%20%20%20%20%22name%22%3A%20%22Practices%2C%20opportunities%20and%20challenges%20in%20the%20fusion%20of%20KGs%20and%20LLMs%22%2C%0D%0A%20%20%20%20%20%20%20%20%22sameAs%22%3A%20%22https%3A%2F%2Fwww.frontiersin.org%2Fjournals%2Fcomputer-science%2Farticles%2F10.3389%2Ffcomp.2025.1590632%2Ffull%22%0D%0A%20%20%20%20%20%20%7D%2C%0D%0A%20%20%20%20%20%20%7B%0D%0A%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22ScholarlyArticle%22%2C%0D%0A%20%20%20%20%20%20%20%20%22name%22%3A%20%22Construction%20of%20a%20knowledge%20graph%20for%20framework%20materials%22%2C%0D%0A%20%20%20%20%20%20%20%20%22sameAs%22%3A%20%22https%3A%2F%2Fwww.nature.com%2Farticles%2Fs41524-025-01540-6%22%0D%0A%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%5D%2C%0D%0A%20%20%20%20%22isBasedOn%22%3A%20%5B%0D%0A%20%20%20%20%20%20%22https%3A%2F%2Farxiv.org%2Fpdf%2F2005.11401%22%0D%0A%20%20%20%20%5D%2C%0D%0A%20%20%20%20%22hasPart%22%3A%20%7B%0D%0A%20%20%20%20%20%20%22%40type%22%3A%20%22FAQPage%22%2C%0D%0A%20%20%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Ffaq%2Fentity-recognition-evidence-weighting-en%22%2C%0D%0A%20%20%20%20%20%20%22mainEntity%22%3A%20%5B%0D%0A%20%20%20%20%20%20%20%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22Question%22%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Ffaq%2Fen-q1%22%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%22name%22%3A%20%22Why%20is%20entity%20recognition%20critical%20for%20AI%20visibility%3F%22%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%22acceptedAnswer%22%3A%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22Answer%22%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Ffaq%2Fen-a1%22%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%22text%22%3A%20%22Without%20reliable%20entity%20detection%20there%20are%20no%20stable%20anchors.%20Only%20when%20mentions%20are%20correctly%20recognised%20as%20concrete%20entities%20can%20they%20be%20linked%20to%20canonical%20IDs%20%28%40id%2C%20Q-IDs%29%20and%20cited%E2%80%94forming%20the%20basis%20for%20evidence-driven%20LLM%20answers.%22%0D%0A%20%20%20%20%20%20%20%20%20%20%7D%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%22about%22%3A%20%7B%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Fentity-recognition%22%20%7D%0D%0A%20%20%20%20%20%20%20%20%7D%2C%0D%0A%20%20%20%20%20%20%20%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22Question%22%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Ffaq%2Fen-q2%22%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%22name%22%3A%20%22What%20is%20the%20difference%20between%20entity%20recognition%20and%20entity%20linking%3F%22%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%22acceptedAnswer%22%3A%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22Answer%22%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Ffaq%2Fen-a2%22%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%22text%22%3A%20%22Entity%20recognition%20detects%20named%20entities%20in%20text.%20Entity%20linking%20resolves%20ambiguity%20by%20connecting%20the%20mention%20to%20a%20canonical%20ID%2C%20such%20as%20a%20Wikidata%20Q-ID.%20Linking%20makes%20entities%20machine-connectable%20and%20citable.%22%0D%0A%20%20%20%20%20%20%20%20%20%20%7D%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%22about%22%3A%20%7B%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Fentity-linking%22%20%7D%0D%0A%20%20%20%20%20%20%20%20%7D%2C%0D%0A%20%20%20%20%20%20%20%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22Question%22%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Ffaq%2Fen-q3%22%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%22name%22%3A%20%22How%20does%20JSON-LD%20increase%20evidential%20weight%3F%22%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%22acceptedAnswer%22%3A%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22Answer%22%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Ffaq%2Fen-a3%22%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%22text%22%3A%20%22JSON-LD%20provides%20structured%2C%20referenceable%20facts%20with%20stable%20IDs%20and%20sameAs%20links.%20LLMs%20can%20prioritise%20these%20signals%20during%20evidence%20extraction%20and%20linking%3B%20this%20boosts%20authority%2C%20interpretability%20and%20the%20likelihood%20of%20being%20cited.%22%0D%0A%20%20%20%20%20%20%20%20%20%20%7D%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%22about%22%3A%20%7B%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Fjson-ld%22%20%7D%0D%0A%20%20%20%20%20%20%20%20%7D%2C%0D%0A%20%20%20%20%20%20%20%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22Question%22%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Ffaq%2Fen-q4%22%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%22name%22%3A%20%22What%20does%20evidence%20weighting%20mean%20in%20the%20LLM%20pipeline%3F%22%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%22acceptedAnswer%22%3A%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22Answer%22%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Ffaq%2Fen-a4%22%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%22text%22%3A%20%22Evidence%20weighting%20evaluates%20competing%20sources%20by%20authority%2C%20freshness%2C%20diversity%20and%20ID%20anchors.%20Formally%2C%20it%20can%20be%20read%20as%20Bayesian%20updating%3A%20incoming%20evidence%20shifts%20prior%20beliefs%20towards%20the%20most%20probable%20claim.%22%0D%0A%20%20%20%20%20%20%20%20%20%20%7D%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%22about%22%3A%20%7B%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Fevidence-weighting%22%20%7D%0D%0A%20%20%20%20%20%20%20%20%7D%2C%0D%0A%20%20%20%20%20%20%20%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22Question%22%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Ffaq%2Fen-q5%22%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%22name%22%3A%20%22What%20role%20does%20RAG%20%28L%2BO%29%20play%20versus%20purely%20parametric%20answers%3F%22%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%22acceptedAnswer%22%3A%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22Answer%22%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Ffaq%2Fen-a5%22%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%22text%22%3A%20%22RAG%20combines%20model%20knowledge%20with%20live%20evidence.%20When%20internal%20confidence%20is%20insufficient%2C%20the%20system%20retrieves%20external%20sources%2C%20extracts%20structured%20evidence%20and%20raises%20citability%20and%20timeliness%20of%20the%20final%20answer.%22%0D%0A%20%20%20%20%20%20%20%20%20%20%7D%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%22about%22%3A%20%7B%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Frag%22%20%7D%0D%0A%20%20%20%20%20%20%20%20%7D%2C%0D%0A%20%20%20%20%20%20%20%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22Question%22%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Ffaq%2Fen-q6%22%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%22name%22%3A%20%22Which%20identifiers%20are%20suitable%20as%20stable%20anchors%3F%22%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%22acceptedAnswer%22%3A%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%22%40type%22%3A%20%22Answer%22%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Ffaq%2Fen-a6%22%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%22text%22%3A%20%22For%20people%3A%20ORCID.%20For%20companies%3A%20LEI.%20For%20securities%3A%20ISIN.%20For%20generic%20entities%20and%20concepts%3A%20Wikidata%20Q-IDs.%20They%20enable%20disambiguation%2C%20stronger%20evidence%20weighting%20and%20consistent%20citation%20across%20systems.%22%0D%0A%20%20%20%20%20%20%20%20%20%20%7D%2C%0D%0A%20%20%20%20%20%20%20%20%20%20%22about%22%3A%20%7B%20%22%40id%22%3A%20%22https%3A%2F%2Fnorbert-kathriner.ch%2Fid%2Fterm%2Fqid%22%20%7D%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%5D%0D%0A%20%20%20%20%7D%0D%0A%20%20%7D%0D%0A%5D%0D%0A%3C%2Fscript%3E%0D%0A[\/vc_raw_html][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overflow=&#8221;visible&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_color=&#8221;#000000&#8243; shape_divider_position=&#8221;top&#8221; shape_divider_height=&#8221;5&#8243; bg_image_animation=&#8221;none&#8221; gradient_type=&#8221;default&#8221; shape_type=&#8221;straight_section&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][vc_column_text css=&#8221;.vc_custom_1762694743163{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_83 counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #1c1c1c;color:#1c1c1c\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #1c1c1c;color:#1c1c1c\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 eztoc-toggle-hide-by-default' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/norbert-kathriner.ch\/en\/entity-recognition-and-evidence-weighting\/#Layers_of_truth_for_machines\" >Layers of truth for machines<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/norbert-kathriner.ch\/en\/entity-recognition-and-evidence-weighting\/#The_three_biggest_misconceptions_in_the_AI_visibility_industry\" >The three biggest misconceptions in the AI visibility industry<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/norbert-kathriner.ch\/en\/entity-recognition-and-evidence-weighting\/#LLM_process_model_Dual-Path_Architecture\" >LLM process model: Dual-Path Architecture<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/norbert-kathriner.ch\/en\/entity-recognition-and-evidence-weighting\/#Fields_of_influence_of_the_truth_layer\" >Fields of influence of the truth layer<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/norbert-kathriner.ch\/en\/entity-recognition-and-evidence-weighting\/#The_mechanics_in_detail_All_steps_from_the_request_to_the_response\" >The mechanics in detail: All steps from the request to the response<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/norbert-kathriner.ch\/en\/entity-recognition-and-evidence-weighting\/#Part_I_%E2%80%93_Case_L_answer_based_on_learning_data\" >Part I &#8211; Case L (answer based on learning data)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/norbert-kathriner.ch\/en\/entity-recognition-and-evidence-weighting\/#Part_II_%E2%80%93_Case_LO_answer_with_online_research\" >Part II &#8211; Case L+O (answer with online research)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/norbert-kathriner.ch\/en\/entity-recognition-and-evidence-weighting\/#Conclusion_From_visibility_to_substance\" >Conclusion: From visibility to substance<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/norbert-kathriner.ch\/en\/entity-recognition-and-evidence-weighting\/#Link_tips\" >Link tips<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/norbert-kathriner.ch\/en\/entity-recognition-and-evidence-weighting\/#Sources\" >Sources<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"Layers_of_truth_for_machines\"><\/span>Layers of truth for machines<span class=\"ez-toc-section-end\"><\/span><\/h2>\n[\/vc_column_text][vc_column_text css=&#8221;.vc_custom_1762726572081{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]Large language models reconstruct the world not through perception, but through probability. They weigh up which statements have stable evidence, are networked and logically consistent &#8211; and use this to form their own algorithmic layer of truth. <strong>This is precisely where the new visibility arises: not through reach, but through evidence.<\/strong><\/p>\n<p>But while AI monitoring tools measure the &#8220;what&#8221; of visibility, which brands appear in LLM responses and AI overviews, the &#8220;how&#8221; and &#8220;why&#8221; remain unclear. They diagnose retrieval signals. <strong>What they do not show is why some sources are only mentioned, while others are recommended.<\/strong> This is because the actual decision is not made in phase 2 (fan-out), but in phases 5-6 (evidence weighting, reasoning). This is where discoverability becomes authority &#8211; or not.<\/p>\n<p>Visibility is not a measured value, but an emergent phenomenon: the result of data architecture, evidential value and semantic coherence. <strong>This article shows how LLMs actually construct answers.<\/strong> Step by step, phase by phase &#8211; and why brands only become part of these answers if they anchor themselves in a structured way. He explains the difference between content that is only read and content that becomes knowledge.<\/p>\n<p><strong>Because machine visibility does not mean being found. It means: belonging to the truth. <\/strong>[\/vc_column_text][vc_row_inner column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; text_align=&#8221;left&#8221; row_position=&#8221;default&#8221; row_position_tablet=&#8221;inherit&#8221; row_position_phone=&#8221;inherit&#8221; overflow=&#8221;visible&#8221; pointer_events=&#8221;all&#8221;][vc_column_inner top_padding_desktop=&#8221;3%&#8221; bottom_padding_desktop=&#8221;3%&#8221; left_padding_desktop=&#8221;3%&#8221; right_padding_desktop=&#8221;3%&#8221; top_padding_phone=&#8221;8%&#8221; bottom_padding_phone=&#8221;8%&#8221; left_padding_phone=&#8221;4%&#8221; right_padding_phone=&#8221;4%&#8221; top_margin=&#8221;3%&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color=&#8221;#008abd&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; font_color=&#8221;#ffffff&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;5px&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; el_class=&#8221;infobox&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;advanced&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;.vc_custom_1762705144696{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221; max_width=&#8221;620&#8243;]\n<h2><span class=\"ez-toc-section\" id=\"The_three_biggest_misconceptions_in_the_AI_visibility_industry\"><\/span>The three biggest misconceptions in the AI visibility industry<span class=\"ez-toc-section-end\"><\/span><\/h2>\n[\/vc_column_text][image_with_animation image_url=&#8221;13075&#8243; image_size=&#8221;full&#8221; max_width=&#8221;custom&#8221; animation_type=&#8221;entrance&#8221; animation=&#8221;None&#8221; animation_movement_type=&#8221;transform_y&#8221; hover_animation=&#8221;none&#8221; alignment=&#8221;&#8221; border_radius=&#8221;none&#8221; box_shadow=&#8221;none&#8221; image_loading=&#8221;default&#8221; max_width_custom=&#8221;88&#8243; max_width_custom_tablet=&#8221;66&#8243; max_width_custom_phone=&#8221;44&#8243; margin_top=&#8221;42&#8243; margin_bottom=&#8221;12&#8243; margin_top_tablet=&#8221;36&#8243; margin_top_phone=&#8221;24&#8243;][vc_column_text css=&#8221;.vc_custom_1762726616209{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]<strong>Misconception 1: AI overviews are an opportunity<\/strong><\/p>\n<p>GEO-optimized brands are found and retrieved &#8211; appearing in fan-out logs and AI overview statistics. But without structured evidence, they remain vague mentions instead of recommendations. They serve as background material, not as a linked source.[\/vc_column_text][image_with_animation image_url=&#8221;13077&#8243; image_size=&#8221;full&#8221; max_width=&#8221;custom&#8221; animation_type=&#8221;entrance&#8221; animation=&#8221;None&#8221; animation_movement_type=&#8221;transform_y&#8221; hover_animation=&#8221;none&#8221; alignment=&#8221;&#8221; border_radius=&#8221;none&#8221; box_shadow=&#8221;none&#8221; image_loading=&#8221;default&#8221; max_width_custom=&#8221;88&#8243; max_width_custom_tablet=&#8221;66&#8243; max_width_custom_phone=&#8221;44&#8243; margin_top=&#8221;24&#8243; margin_bottom=&#8221;12&#8243;][vc_column_text css=&#8221;.vc_custom_1762704935976{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]<strong>Misconception 2: LLMs are black boxes &#8211; you can only experiment<\/strong><\/p>\n<p>Trial &amp; error makes no sense: test prompt variations, A\/B test content, hope. Without an understanding of the process, you&#8217;re flying blind when it comes to optimization. If you don&#8217;t know when Mention vs. Recommendation is decided, you are investing in the wrong levers. If you don&#8217;t understand entity recognition, you produce content that is unambiguous for humans but remains ambiguous for machines.[\/vc_column_text][image_with_animation image_url=&#8221;13079&#8243; image_size=&#8221;full&#8221; max_width=&#8221;custom&#8221; animation_type=&#8221;entrance&#8221; animation=&#8221;None&#8221; animation_movement_type=&#8221;transform_y&#8221; hover_animation=&#8221;none&#8221; alignment=&#8221;&#8221; border_radius=&#8221;none&#8221; box_shadow=&#8221;none&#8221; image_loading=&#8221;default&#8221; max_width_custom=&#8221;88&#8243; max_width_custom_tablet=&#8221;66&#8243; max_width_custom_phone=&#8221;44&#8243; margin_top=&#8221;24&#8243; margin_bottom=&#8221;12&#8243;][vc_column_text css=&#8221;.vc_custom_1762704944914{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]<strong>Misconception 3: AI visibility is short-term &#8211; training cycles are irrelevant<\/strong><\/p>\n<p>Monitoring shows visibility today. What it doesn&#8217;t show: Who will be cited in 8-12 months even without online research? Brands with consistent Q-IDs and Schema.org relations form the parametric knowledge base of future models. They will become part of the &#8220;solidified layer of truth&#8221; &#8211; cited without research.[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overflow=&#8221;visible&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_color=&#8221;#000000&#8243; shape_divider_position=&#8221;top&#8221; shape_divider_height=&#8221;5&#8243; bg_image_animation=&#8221;none&#8221; gradient_type=&#8221;default&#8221; shape_type=&#8221;straight_section&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][vc_column_text css=&#8221;.vc_custom_1762691205855{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h2><span class=\"ez-toc-section\" id=\"LLM_process_model_Dual-Path_Architecture\"><\/span>LLM process model: Dual-Path Architecture<span class=\"ez-toc-section-end\"><\/span><\/h2>\n[\/vc_column_text][vc_column_text css=&#8221;.vc_custom_1762691578115{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]<strong>Visualization of the answer formation | Case L vs. Case L+O<\/strong>[\/vc_column_text][vc_column_text css=&#8221;.vc_custom_1762708612506{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]When a Large Language Model answers a query, it is faced with a fundamental decision: Is the internally stored knowledge sufficient &#8211; or must additional research be carried out on the web? This decision determines which of two fundamentally different paths is taken.<\/p>\n<p><strong>Case L (learning data)<\/strong> uses only the parametric knowledge from the training &#8211; a condensed image of human language and knowledge structures that is typically several months to over a year old. Updating is not continuous, but takes place in discrete training cycles. If the model is from 2025, its training knowledge is available until around mid-2024.<\/p>\n<p><strong>Case L+O (Learning Data + Online)<\/strong> supplements this static knowledge with real-time research on the web. The model opens a &#8220;candidate space&#8221; of URLs, scans their content and extracts current evidence. It recognizes structured data such as JSON-LD, Schema.org markup and entity anchors (Q-IDs, sameAs) in real time &#8211; long before they flow into future training cycles.<\/p>\n<p>The following visualization shows both paths in detail: from the initial prompt to the critical decision (confidence threshold) to the final answer. What looks like a linear process at first glance is actually a complex system with feedback loops, evidence weighting and semantic synthesis &#8211; and this is precisely where it is decided whether structured data leads to a mere mention or an authoritative recommendation.[\/vc_column_text][\/vc_column][\/vc_row][vc_row type=&#8221;full_width_content&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overflow=&#8221;visible&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_color=&#8221;#000000&#8243; shape_divider_position=&#8221;top&#8221; shape_divider_height=&#8221;5&#8243; bg_image_animation=&#8221;none&#8221; gradient_type=&#8221;default&#8221; shape_type=&#8221;straight_section&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; top_margin=&#8221;3%&#8221; bottom_margin=&#8221;3%&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; sticky_content=&#8221;true&#8221; sticky_content_functionality=&#8221;css&#8221; sticky_content_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/2&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_raw_html css=&#8221;&#8221;]%3C%21DOCTYPE%20html%3E%0D%0A%3Chtml%20lang%3D%22en%22%3E%0D%0A%3Chead%3E%0D%0A%20%20%20%20%3Cmeta%20charset%3D%22UTF-8%22%3E%0D%0A%20%20%20%20%3Cmeta%20name%3D%22viewport%22%20content%3D%22width%3Ddevice-width%2C%20initial-scale%3D1.0%22%3E%0D%0A%20%20%20%20%3Ctitle%3ELLM%20Dual-Path%20Model%20-%20Mobile%20Infographic%3C%2Ftitle%3E%0D%0A%20%20%20%20%3Clink%20rel%3D%22preconnect%22%20href%3D%22https%3A%2F%2Ffonts.googleapis.com%22%3E%0D%0A%20%20%20%20%3Clink%20rel%3D%22preconnect%22%20href%3D%22https%3A%2F%2Ffonts.gstatic.com%22%20crossorigin%3E%0D%0A%20%20%20%20%3Clink%20href%3D%22https%3A%2F%2Ffonts.googleapis.com%2Fcss2%3Ffamily%3DPoppins%3Awght%40400%3B600%3B700%26display%3Dswap%22%20rel%3D%22stylesheet%22%3E%0D%0A%20%20%20%20%3Cstyle%3E%0D%0A%20%20%20%20%20%20%20%20%2A%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20margin%3A%200%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20padding%3A%200%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20box-sizing%3A%20border-box%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20body%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20font-family%3A%20%27Poppins%27%2C%20sans-serif%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20background%3A%20%23ffffff%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20display%3A%20flex%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20justify-content%3A%20center%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20padding%3A%200%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.infographic%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20width%3A%20420px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20background%3A%20%23ffffff%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20position%3A%20relative%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.legend%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20display%3A%20flex%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20justify-content%3A%20center%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20gap%3A%2012px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20padding%3A%2024px%2016px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20flex-wrap%3A%20wrap%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.legend-badge%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20display%3A%20flex%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20align-items%3A%20center%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20gap%3A%208px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20padding%3A%208px%2014px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20border-radius%3A%208px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20font-size%3A%2011px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20font-weight%3A%20600%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.legend-badge.blue%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20background%3A%20%23e0e7ff%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20color%3A%20%234c51bf%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.legend-badge.orange%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20background%3A%20%23fef3c7%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20color%3A%20%23d97706%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.legend-badge.green%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20background%3A%20%23d1fae5%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20color%3A%20%23059669%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.legend-dot%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20width%3A%2012px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20height%3A%2012px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20border-radius%3A%2050%25%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.legend-dot.blue%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20background%3A%20linear-gradient%28135deg%2C%20%23667eea%200%25%2C%20%23764ba2%20100%25%29%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.legend-dot.orange%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20background%3A%20linear-gradient%28135deg%2C%20%23f59e0b%200%25%2C%20%23dc2626%20100%25%29%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.legend-dot.green%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20background%3A%20linear-gradient%28135deg%2C%20%2310b981%200%25%2C%20%23059669%20100%25%29%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.phase-container%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20padding%3A%200%2016px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20position%3A%20relative%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.phase-box%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20background%3A%20%23ffffff%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20border-radius%3A%2012px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20padding%3A%2018px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20margin-bottom%3A%2016px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20box-shadow%3A%200%202px%208px%20rgba%280%2C0%2C0%2C0.08%29%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20border-left%3A%204px%20solid%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20position%3A%20relative%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.phase-box.blue%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20border-left-color%3A%20%23667eea%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20background%3A%20linear-gradient%28to%20right%2C%20%23f7f9ff%200%25%2C%20%23ffffff%20100%25%29%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.phase-box.orange%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20border-left-color%3A%20%23f59e0b%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20background%3A%20linear-gradient%28to%20right%2C%20%23fffbf0%200%25%2C%20%23ffffff%20100%25%29%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.phase-box.green%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20border-left-color%3A%20%2310b981%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20background%3A%20linear-gradient%28to%20right%2C%20%23f0fdf9%200%25%2C%20%23ffffff%20100%25%29%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.phase-number%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20position%3A%20absolute%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20top%3A%20-8px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20left%3A%2012px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20background%3A%20%23ffffff%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20padding%3A%202px%2010px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20border-radius%3A%2012px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20font-size%3A%2010px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20font-weight%3A%20700%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20color%3A%20%23718096%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20border%3A%202px%20solid%20%23e2e8f0%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.phase-title%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20font-size%3A%2013px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20font-weight%3A%20700%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20color%3A%20%232d3748%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20margin-bottom%3A%206px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20line-height%3A%201.3%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.phase-desc%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20font-size%3A%2011px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20color%3A%20%234a5568%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20line-height%3A%201.5%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.arrow-down%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20width%3A%202px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20height%3A%2016px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20background%3A%20%23cbd5e0%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20margin%3A%200%20auto%2016px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20position%3A%20relative%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.arrow-down%3A%3Aafter%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20content%3A%20%27%27%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20position%3A%20absolute%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20bottom%3A%20-6px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20left%3A%2050%25%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20transform%3A%20translateX%28-50%25%29%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20width%3A%200%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20height%3A%200%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20border-left%3A%205px%20solid%20transparent%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20border-right%3A%205px%20solid%20transparent%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20border-top%3A%206px%20solid%20%23cbd5e0%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.arrow-down.blue%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20background%3A%20%23667eea%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.arrow-down.blue%3A%3Aafter%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20border-top-color%3A%20%23667eea%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.arrow-down.orange%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20background%3A%20%23f59e0b%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.arrow-down.orange%3A%3Aafter%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20border-top-color%3A%20%23f59e0b%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.decision-node%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20width%3A%20160px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20height%3A%20160px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20margin%3A%2016px%20auto%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20position%3A%20relative%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20display%3A%20flex%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20align-items%3A%20center%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20justify-content%3A%20center%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.decision-diamond%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20width%3A%20120px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20height%3A%20120px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20background%3A%20linear-gradient%28135deg%2C%20%23fbbf24%200%25%2C%20%23f59e0b%20100%25%29%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20transform%3A%20rotate%2845deg%29%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20border-radius%3A%2012px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20box-shadow%3A%200%204px%2012px%20rgba%28245%2C%20158%2C%2011%2C%200.3%29%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20position%3A%20absolute%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.decision-text%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20position%3A%20relative%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20z-index%3A%201%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20text-align%3A%20center%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20font-size%3A%2011px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20font-weight%3A%20700%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20color%3A%20%23ffffff%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20line-height%3A%201.4%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20padding%3A%200%2020px%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.split-container%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20display%3A%20grid%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20grid-template-columns%3A%201fr%201fr%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20gap%3A%2012px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20margin%3A%2016px%200%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.split-label%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20text-align%3A%20center%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20font-size%3A%2010px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20font-weight%3A%20700%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20color%3A%20%23718096%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20text-transform%3A%20uppercase%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20letter-spacing%3A%200.5px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20margin-bottom%3A%2012px%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.split-label.blue%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20color%3A%20%234c51bf%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.split-label.orange%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20color%3A%20%23d97706%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.phase-box.compact%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20padding%3A%2014px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20margin-bottom%3A%2012px%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.phase-box.compact%20.phase-title%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20font-size%3A%2011px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20margin-bottom%3A%204px%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.phase-box.compact%20.phase-desc%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20font-size%3A%2010px%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.merge-arrow%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20width%3A%20100%25%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20height%3A%2040px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20position%3A%20relative%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20margin%3A%2016px%200%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.merge-arrow%20svg%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20width%3A%20100%25%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20height%3A%20100%25%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.feedback-loop%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20background%3A%20%23fee2e2%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20border%3A%202px%20dashed%20%23dc2626%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20border-radius%3A%208px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20padding%3A%2012px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20margin%3A%2016px%200%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20text-align%3A%20center%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.feedback-loop-title%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20font-size%3A%2010px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20font-weight%3A%20700%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20color%3A%20%23dc2626%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20margin-bottom%3A%206px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20text-transform%3A%20uppercase%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20letter-spacing%3A%200.5px%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.feedback-loop-text%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20font-size%3A%2010px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20color%3A%20%23991b1b%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20line-height%3A%201.4%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20.skip-indicator%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20background%3A%20%23f7fafc%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20border%3A%202px%20dashed%20%23cbd5e0%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20border-radius%3A%208px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20padding%3A%2012px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20text-align%3A%20center%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20font-size%3A%2010px%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20color%3A%20%23718096%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20font-style%3A%20italic%3B%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20%40media%20print%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20body%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20background%3A%20white%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20.infographic%20%7B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20box-shadow%3A%20none%3B%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%20%20%20%20%7D%0D%0A%20%20%20%20%3C%2Fstyle%3E%0D%0A%3C%2Fhead%3E%0D%0A%3Cbody%3E%0D%0A%20%20%20%20%3Cdiv%20class%3D%22infographic%22%3E%0D%0A%20%20%20%20%20%20%20%20%3C%21--%20Legend%20--%3E%0D%0A%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22legend%22%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22legend-badge%20blue%22%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22legend-dot%20blue%22%3E%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cspan%3ECase%20L%3C%2Fspan%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22legend-badge%20orange%22%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22legend-dot%20orange%22%3E%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cspan%3ECase%20L%2BO%3C%2Fspan%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22legend-badge%20green%22%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22legend-dot%20green%22%3E%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cspan%3EShared%3C%2Fspan%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-container%22%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%3C%21--%20Phase%200%20--%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-box%20green%22%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-number%22%3E0%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-title%22%3EPrompt%20Input%20%2F%20Intent%20Parsing%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-desc%22%3ESemantic%20analysis%2C%20intent%20vector%20creation%2C%20complexity%20assessment%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22arrow-down%22%3E%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%3C%21--%20Phase%201%20--%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-box%20green%22%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-number%22%3E1%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-title%22%3EInternal%20Knowledge%20Retrieval%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-desc%22%3ERetrieve%20parametric%20knowledge%2C%20calculate%20confidence%20score%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22arrow-down%22%3E%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%3C%21--%20Decision%20Node%20--%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22decision-node%22%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22decision-diamond%22%3E%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22decision-text%22%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20Confidence%3Cbr%3E%3E%3Cbr%3EThreshold%3F%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%3C%21--%20Split%3A%20Case%20L%20%2F%20Case%20L%2BO%20--%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20style%3D%22display%3A%20grid%3B%20grid-template-columns%3A%201fr%201fr%3B%20gap%3A%2012px%3B%20margin-bottom%3A%2016px%3B%22%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22split-label%20blue%22%3E%E2%86%90%20Case%20L%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22split-label%20orange%22%3ECase%20L%2BO%20%E2%86%92%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22split-container%22%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3C%21--%20Case%20L%3A%20Left%20Column%20--%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22skip-indicator%22%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20Phase%202-3%3Cbr%3Eskipped%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20style%3D%22height%3A%2012px%3B%22%3E%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-box%20blue%20compact%22%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-number%22%3E4%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-title%22%3EEntity%20Recognition%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-desc%22%3ETrained%20patterns%20%28Wikipedia%2C%20Wikidata%29%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22arrow-down%20blue%22%20style%3D%22height%3A%2012px%3B%20margin%3A%208px%20auto%3B%22%3E%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-box%20blue%20compact%22%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-number%22%3E5%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-title%22%3EEvidence%20Weighting%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-desc%22%3EInternal%20frequency%20%26%20coherence%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22arrow-down%20blue%22%20style%3D%22height%3A%2012px%3B%20margin%3A%208px%20auto%3B%22%3E%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-box%20blue%20compact%22%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-number%22%3E6%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-title%22%3EReasoning%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-desc%22%3EParametric%20synthesis%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3C%21--%20Case%20L%2BO%3A%20Right%20Column%20--%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-box%20orange%20compact%22%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-number%22%3E2%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-title%22%3EFan-Out%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-desc%22%3EQuery%20generation%20%26%20web%20search%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22arrow-down%20orange%22%20style%3D%22height%3A%2012px%3B%20margin%3A%208px%20auto%3B%22%3E%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-box%20orange%20compact%22%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-number%22%3E3%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-title%22%3EEvidence%20Extraction%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-desc%22%3EJSON-LD%2C%20RDFa%2C%20passage%20ranking%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22arrow-down%20orange%22%20style%3D%22height%3A%2012px%3B%20margin%3A%208px%20auto%3B%22%3E%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-box%20orange%20compact%22%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-number%22%3E4%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-title%22%3EEntity%20Linking%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-desc%22%3E%40id%2C%20sameAs%2C%20Q-IDs%20verification%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22arrow-down%20orange%22%20style%3D%22height%3A%2012px%3B%20margin%3A%208px%20auto%3B%22%3E%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-box%20orange%20compact%22%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-number%22%3E5%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-title%22%3EEvidence%20Weighting%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-desc%22%3EEvidence%20architecture%20%2B%20source%20diversity%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22arrow-down%20orange%22%20style%3D%22height%3A%2012px%3B%20margin%3A%208px%20auto%3B%22%3E%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-box%20orange%20compact%22%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-number%22%3E5a%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-title%22%3EResponse%20Planning%20%E2%86%BB%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-desc%22%3EEvidence-guided%20outline%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22arrow-down%20orange%22%20style%3D%22height%3A%2012px%3B%20margin%3A%208px%20auto%3B%22%3E%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-box%20orange%20compact%22%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-number%22%3E6%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-title%22%3EReasoning%20%E2%86%BB%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-desc%22%3EFusion%20L%2BO%20%2B%20multi-hop%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%3C%21--%20Feedback%20Loops%20--%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22feedback-loop%22%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22feedback-loop-title%22%3E%E2%86%BB%20Feedback%20Loops%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22feedback-loop-text%22%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20Phase%205%20%E2%86%92%20Phase%202%20%28on%20low%20evidence%29%3Cbr%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20Phase%206%20%E2%86%92%20Phase%203%20%28on%20inconsistencies%29%3Cbr%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20Phase%205a%20%E2%86%92%20Phase%205%20%28on%20unclear%20structure%29%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%3C%21--%20Merge%20--%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22merge-arrow%22%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Csvg%20viewBox%3D%220%200%20388%2040%22%20fill%3D%22none%22%20xmlns%3D%22http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg%22%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3C%21--%20Left%20path%20%28blue%29%20--%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cpath%20d%3D%22M%2090%200%20Q%2090%2020%2C%20194%2040%22%20stroke%3D%22%23667eea%22%20stroke-width%3D%222%22%20fill%3D%22none%22%2F%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3C%21--%20Right%20path%20%28orange%29%20--%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cpath%20d%3D%22M%20298%200%20Q%20298%2020%2C%20194%2040%22%20stroke%3D%22%23f59e0b%22%20stroke-width%3D%222%22%20fill%3D%22none%22%2F%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3C%21--%20Arrow%20--%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cpolygon%20points%3D%22194%2C40%20189%2C33%20199%2C33%22%20fill%3D%22%2310b981%22%2F%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3C%2Fsvg%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%3C%21--%20Phase%207%20--%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-box%20green%22%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-number%22%3E7%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-title%22%3EFinal%20Response%20Construction%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22phase-desc%22%3ELinguistic%20optimization%2C%20style%20alignment%2C%20optional%20citation%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%20%20%20%20%3C%2Fdiv%3E%0D%0A%20%20%20%20%20%20%20%20%3C%2Fdiv%3E%0D%0A%20%20%20%20%3C%2Fdiv%3E%0D%0A%3C%2Fbody%3E%0D%0A%3C%2Fhtml%3E[\/vc_raw_html][\/vc_column][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/2&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][nectar_responsive_text inherited_font_style=&#8221;default&#8221; text_direction=&#8221;default&#8221;]\n<h3>Phase details<\/h3>\n<p><strong>Phase 0: Prompt Input \/ Intent Parsing<\/strong><\/p>\n<p>Tokenization \u2192 Semantic analysis \u2192 Intent vector creation \u2192 Complexity assessment. The intent vector controls all subsequent layers. [divider line_type=&#8221;full-width&#8221; custom_height=&#8221;10&#8243;]\n<p><strong>Phase 1: Internal Knowledge Retrieval<\/strong><\/p>\n<p>Access to parametric knowledge (trained weightings). Confidence score decides on Case L vs. L+O. With Confidence &gt; Threshold, the system remains in Case L. [divider line_type=&#8221;full-width&#8221; custom_height=&#8221;10&#8243;]\n<p><strong>Decision node: Confidence Threshold<\/strong><\/p>\n<p>Is internal knowledge sufficient? YES \u2192 Case L (learning data only). NO \u2192 Case L+O (additional online research). This switch is critical for path selection. [divider line_type=&#8221;full-width&#8221; custom_height=&#8221;10&#8243;]\n<p><strong>Phase 2: Fan-Out \/ Candidate Retrieval<\/strong><\/p>\n<p>Generation of semantic search queries (1-6 tokens). Query of external search interfaces. Result: 3-10 prioritized URLs + snippets. This is where the external evidence search begins. [divider line_type=&#8221;full-width&#8221; custom_height=&#8221;10&#8243;]\n<p><strong>Phase 3: Source Evaluation \/ Evidence Extraction<\/strong><\/p>\n<p>HTML parsing + JSON-LD extraction + RDFa\/microdata analysis. Structured data (Schema.org) increases evidential value. Passage ranking according to relevance to the intent vector. [divider line_type=&#8221;full-width&#8221; custom_height=&#8221;10&#8243;]\n<p><strong>Phase 4: Entity Recognition (Case L)<\/strong><\/p>\n<p>Use of trained entity patterns from Wikipedia, Wikidata, Common Crawl. Internal Q-ID matching based on training. No external verification. [divider line_type=&#8221;full-width&#8221; custom_height=&#8221;10&#8243;]\n<p><strong>Phase 4: Entity Recognition &amp; Linking (Case L+O)<\/strong><\/p>\n<p>In addition to internal matching: verification via @id, sameAs, Q-IDs from JSON-LD. Cross-document entity coreference. The AI Visibility layer works directly here. [divider line_type=&#8221;full-width&#8221; custom_height=&#8221;10&#8243;]\n<p><strong>Phase 5: Evidence Weighting (Case L)<\/strong><\/p>\n<p>Weighting according to internal frequency (how often the pattern occurred in the training) and coherence (consistency in parametric knowledge).[divider line_type=&#8221;full-width&#8221; custom_height=&#8221;10&#8243;]\n<p><strong>Phase 5: Evidence Weighting (Case L+O)<\/strong><\/p>\n<p>Document architecture analysis: Sources with Q-IDs, sameAs, identifier receive 2-3x higher weight. Source diversity + topicality + domain authority are included in the evidence matrix. [divider line_type=&#8221;full-width&#8221; custom_height=&#8221;10&#8243;]\n<p><strong>Phase 5a: Response Planning (Case L+O)<\/strong><\/p>\n<p>Evidence-guided outline: Claims are prioritized according to the evidence matrix. Hierarchical relations (isPartOf, about) structure the argumentation. Feedback loop to phase 5 possible. [divider line_type=&#8221;full-width&#8221; custom_height=&#8221;10&#8243;]\n<p><strong>Phase 6: Reasoning (Case L)<\/strong><\/p>\n<p>Composition from learned patterns. Statistically coherent response based on frequency weighting in training. No external evidence.<\/p>\n<p><strong>Phase 6: Reasoning &amp; Synthesis (Case L+O)<\/strong><\/p>\n<p>Fusion of internal (L) and external (O) evidence. Conflict resolution for contradictions. Multi-hop reasoning via entity graphs. This is where the decision is made: Mention vs. recommendation. Feedback loop to phase 3 possible. [divider line_type=&#8221;full-width&#8221; custom_height=&#8221;10&#8243;]\n<p><strong>Phase 7: Final Response Construction<\/strong><\/p>\n<p>Linguistic optimization + style alignment. In Case L+O: optional citation with url, name, publisher from Schema.org. Structured metadata enables explicit source citation. [divider line_type=&#8221;full-width&#8221; custom_height=&#8221;10&#8243;]\n<h3>Feedback loops<\/h3>\n<p><strong>Phase 5 \u2192 Phase 2: <\/strong><\/p>\n<p>In case of insufficient evidence (low score in evidence matrix), return to fan-out with refined queries.[divider line_type=&#8221;full-width&#8221; custom_height=&#8221;10&#8243;]\n<p><strong>Phase 6 \u2192 Phase 3: <\/strong><\/p>\n<p>In case of inconsistencies or contradictions during reasoning: additional source retrievals or re-ranking.[divider line_type=&#8221;full-width&#8221; custom_height=&#8221;10&#8243;]\n<p><strong>Phase 5a \u2192 Phase 5: <\/strong><\/p>\n<p>If the argumentation structure is unclear: Re-assessment of the evidence weighting.[divider line_type=&#8221;full-width&#8221; custom_height=&#8221;10&#8243;][\/nectar_responsive_text][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; top_padding=&#8221;2%&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overflow=&#8221;visible&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_color=&#8221;#000000&#8243; shape_divider_position=&#8221;top&#8221; shape_divider_height=&#8221;5&#8243; bg_image_animation=&#8221;none&#8221; gradient_type=&#8221;default&#8221; shape_type=&#8221;straight_section&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; top_margin=&#8221;30&#8243; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;.vc_custom_1762700208148{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h2><span class=\"ez-toc-section\" id=\"Fields_of_influence_of_the_truth_layer\"><\/span>Fields of influence of the truth layer<span class=\"ez-toc-section-end\"><\/span><\/h2>\n[\/vc_column_text][vc_column_text css=&#8221;.vc_custom_1762691811412{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]<strong>Structured data does not only work in one of the two modes &#8211; it influences the response formation on two parallel time axes and in both paths.<\/strong>[\/vc_column_text][vc_column_text css=&#8221;.vc_custom_1762707142012{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]<strong>Transient (Case L+O):<\/strong> When the LLM searches online, it reads JSON-LD, Schema.org markup and entity anchors such as @id, sameAs or Q-IDs in real time. This structured data flows immediately into the evidence weighting: sources with stable identifiers receive 2-3x higher weight, cross-document entity coreference becomes possible, and the document architecture decides on mention vs. recommendation. This effect is transient &#8211; it only exists for the duration of the current search.<\/p>\n<p><strong>Persistent (Case L):<\/strong> Structured data that is present over a longer period of time is integrated into the parametric knowledge base in future training cycles. They become part of the &#8220;solidified layer of truth&#8221; that the model can use even without online access. Entities with consistent Q-IDs, sameAs links and hierarchical relations then shape the internal frequency weighting and entity recognition in Case L. This effect is persistent &#8211; it shapes the model in the long term.<\/p>\n<p>The following illustration shows in which specific phases (layers 4-7) structured data is effective &#8211; and how it permeates the entire reasoning process from mere identification to evidence evaluation and final citation. What is often misunderstood as &#8220;SEO for LLMs&#8221; is in fact semantic infrastructure: those who build up machine-readable evidence today secure both immediate visibility and long-term reputation.[\/vc_column_text][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; top_padding=&#8221;2%&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overflow=&#8221;visible&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_color=&#8221;#000000&#8243; shape_divider_position=&#8221;top&#8221; shape_divider_height=&#8221;5&#8243; bg_image_animation=&#8221;none&#8221; gradient_type=&#8221;default&#8221; shape_type=&#8221;straight_section&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; top_margin=&#8221;30&#8243; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_row_inner equal_height=&#8221;yes&#8221; content_placement=&#8221;bottom&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; text_align=&#8221;left&#8221; row_position=&#8221;default&#8221; row_position_tablet=&#8221;inherit&#8221; row_position_phone=&#8221;inherit&#8221; overflow=&#8221;visible&#8221; pointer_events=&#8221;all&#8221;][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; bottom_margin=&#8221;-10&#8243; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][nectar_icon icon_family=&#8221;fontawesome&#8221; icon_style=&#8221;default&#8221; icon_color_type=&#8221;color_scheme&#8221; icon_color=&#8221;Accent-Color&#8221; icon_padding=&#8221;0px&#8221; pointer_events=&#8221;all&#8221; icon_fontawesome=&#8221;fa fa-star&#8221; icon_size=&#8221;14&#8243; margin_right=&#8221;5&#8243;][nectar_icon icon_family=&#8221;fontawesome&#8221; icon_style=&#8221;default&#8221; icon_color_type=&#8221;color_scheme&#8221; icon_color=&#8221;Accent-Color&#8221; icon_padding=&#8221;0px&#8221; pointer_events=&#8221;all&#8221; icon_fontawesome=&#8221;fa fa-star&#8221; icon_size=&#8221;14&#8243; margin_right=&#8221;5&#8243;][nectar_icon icon_family=&#8221;fontawesome&#8221; icon_style=&#8221;default&#8221; icon_color_type=&#8221;color_scheme&#8221; icon_color=&#8221;Accent-Color&#8221; icon_padding=&#8221;0px&#8221; pointer_events=&#8221;all&#8221; icon_fontawesome=&#8221;fa fa-star&#8221; icon_size=&#8221;14&#8243; margin_right=&#8221;5&#8243;][nectar_icon icon_family=&#8221;fontawesome&#8221; icon_style=&#8221;default&#8221; icon_color_type=&#8221;color_scheme&#8221; icon_color=&#8221;Accent-Color&#8221; icon_padding=&#8221;0px&#8221; pointer_events=&#8221;all&#8221; icon_fontawesome=&#8221;fa fa-star&#8221; icon_size=&#8221;14&#8243; margin_right=&#8221;5&#8243;][nectar_icon icon_family=&#8221;fontawesome&#8221; icon_style=&#8221;default&#8221; icon_color_type=&#8221;color_scheme&#8221; icon_color=&#8221;Accent-Color&#8221; icon_padding=&#8221;0px&#8221; pointer_events=&#8221;all&#8221; icon_fontawesome=&#8221;fa fa-star-half-o&#8221; icon_size=&#8221;14&#8243;][vc_column_text css=&#8221;.vc_custom_1762701075762{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h3>Layer 4: Entity Recognition<\/h3>\n[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][vc_row_inner equal_height=&#8221;yes&#8221; column_margin=&#8221;5px&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; text_align=&#8221;left&#8221; row_position=&#8221;default&#8221; row_position_tablet=&#8221;inherit&#8221; row_position_phone=&#8221;inherit&#8221; overflow=&#8221;visible&#8221; pointer_events=&#8221;all&#8221;][vc_column_inner column_padding=&#8221;padding-3-percent&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color=&#8221;#C1CFDC&#8221; background_color_opacity=&#8221;0.3&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/2&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;.vc_custom_1762700375325{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h5>Mechanism<\/h5>\n<p>Identification &amp; anchoring of entities[\/vc_column_text][\/vc_column_inner][vc_column_inner column_padding=&#8221;padding-3-percent&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color=&#8221;#C1CFDC&#8221; background_color_opacity=&#8221;0.3&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/2&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;.vc_custom_1762700427152{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h5>Data objects<\/h5>\n[\/vc_column_text][nectar_badge display_tag=&#8221;body&#8221; badge_style=&#8221;default&#8221; bg_color_type=&#8221;global&#8221; color=&#8221;extra-color-gradient-2&#8243; text_color=&#8221;#ffffff&#8221; padding=&#8221;medium&#8221; border_radius=&#8221;3px&#8221; display=&#8221;inline&#8221; text=&#8221;@id&#8221; margin_right=&#8221;2&#8243; margin_bottom=&#8221;2&#8243;][nectar_badge display_tag=&#8221;body&#8221; badge_style=&#8221;default&#8221; bg_color_type=&#8221;global&#8221; color=&#8221;extra-color-gradient-2&#8243; text_color=&#8221;#ffffff&#8221; padding=&#8221;medium&#8221; border_radius=&#8221;3px&#8221; display=&#8221;inline&#8221; text=&#8221;sameAs&#8221; margin_right=&#8221;2&#8243; margin_bottom=&#8221;2&#8243;][nectar_badge display_tag=&#8221;body&#8221; badge_style=&#8221;default&#8221; bg_color_type=&#8221;global&#8221; color=&#8221;extra-color-gradient-2&#8243; text_color=&#8221;#ffffff&#8221; padding=&#8221;medium&#8221; border_radius=&#8221;3px&#8221; display=&#8221;inline&#8221; text=&#8221;Q-IDs&#8221; margin_right=&#8221;2&#8243; margin_bottom=&#8221;2&#8243;][nectar_badge display_tag=&#8221;body&#8221; badge_style=&#8221;default&#8221; bg_color_type=&#8221;global&#8221; color=&#8221;extra-color-gradient-2&#8243; text_color=&#8221;#ffffff&#8221; padding=&#8221;medium&#8221; border_radius=&#8221;3px&#8221; display=&#8221;inline&#8221; text=&#8221;ISIN&#8221; margin_right=&#8221;2&#8243; margin_bottom=&#8221;2&#8243;][nectar_badge display_tag=&#8221;body&#8221; badge_style=&#8221;default&#8221; bg_color_type=&#8221;global&#8221; color=&#8221;extra-color-gradient-2&#8243; text_color=&#8221;#ffffff&#8221; padding=&#8221;medium&#8221; border_radius=&#8221;3px&#8221; display=&#8221;inline&#8221; text=&#8221;LEI&#8221; margin_right=&#8221;2&#8243; margin_bottom=&#8221;2&#8243;][\/vc_column_inner][\/vc_row_inner][vc_row_inner column_margin=&#8221;5px&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; text_align=&#8221;left&#8221; row_position=&#8221;default&#8221; row_position_tablet=&#8221;inherit&#8221; row_position_phone=&#8221;inherit&#8221; overflow=&#8221;visible&#8221; pointer_events=&#8221;all&#8221;][vc_column_inner column_padding=&#8221;padding-3-percent&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color=&#8221;#C1CFDC&#8221; background_color_opacity=&#8221;0.3&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;.vc_custom_1762700399894{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h5>Mode of action<\/h5>\n<p>Immediate evidence (transient) + long-term effect (persistent). Unique IDs prevent confusion.[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; top_padding=&#8221;2%&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overflow=&#8221;visible&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_color=&#8221;#000000&#8243; shape_divider_position=&#8221;top&#8221; shape_divider_height=&#8221;5&#8243; bg_image_animation=&#8221;none&#8221; gradient_type=&#8221;default&#8221; shape_type=&#8221;straight_section&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; top_margin=&#8221;30&#8243; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_row_inner equal_height=&#8221;yes&#8221; content_placement=&#8221;bottom&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; text_align=&#8221;left&#8221; row_position=&#8221;default&#8221; row_position_tablet=&#8221;inherit&#8221; row_position_phone=&#8221;inherit&#8221; overflow=&#8221;visible&#8221; pointer_events=&#8221;all&#8221;][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; bottom_margin=&#8221;-10&#8243; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][nectar_icon icon_family=&#8221;fontawesome&#8221; icon_style=&#8221;default&#8221; icon_color_type=&#8221;color_scheme&#8221; icon_color=&#8221;Accent-Color&#8221; icon_padding=&#8221;0px&#8221; pointer_events=&#8221;all&#8221; icon_fontawesome=&#8221;fa fa-star&#8221; icon_size=&#8221;14&#8243; margin_right=&#8221;5&#8243;][nectar_icon icon_family=&#8221;fontawesome&#8221; icon_style=&#8221;default&#8221; icon_color_type=&#8221;color_scheme&#8221; icon_color=&#8221;Accent-Color&#8221; icon_padding=&#8221;0px&#8221; pointer_events=&#8221;all&#8221; icon_fontawesome=&#8221;fa fa-star&#8221; icon_size=&#8221;14&#8243; margin_right=&#8221;5&#8243;][nectar_icon icon_family=&#8221;fontawesome&#8221; icon_style=&#8221;default&#8221; icon_color_type=&#8221;color_scheme&#8221; icon_color=&#8221;Accent-Color&#8221; icon_padding=&#8221;0px&#8221; pointer_events=&#8221;all&#8221; icon_fontawesome=&#8221;fa fa-star&#8221; icon_size=&#8221;14&#8243; margin_right=&#8221;5&#8243;][nectar_icon icon_family=&#8221;fontawesome&#8221; icon_style=&#8221;default&#8221; icon_color_type=&#8221;color_scheme&#8221; icon_color=&#8221;Accent-Color&#8221; icon_padding=&#8221;0px&#8221; pointer_events=&#8221;all&#8221; icon_fontawesome=&#8221;fa fa-star&#8221; icon_size=&#8221;14&#8243; margin_right=&#8221;5&#8243;][nectar_icon icon_family=&#8221;fontawesome&#8221; icon_style=&#8221;default&#8221; icon_color_type=&#8221;color_scheme&#8221; icon_color=&#8221;Accent-Color&#8221; icon_padding=&#8221;0px&#8221; pointer_events=&#8221;all&#8221; icon_fontawesome=&#8221;fa fa-star&#8221; icon_size=&#8221;14&#8243;][vc_column_text css=&#8221;.vc_custom_1762701550560{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h3>Layer 5: Evidence Weighting<\/h3>\n[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][vc_row_inner equal_height=&#8221;yes&#8221; column_margin=&#8221;5px&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; text_align=&#8221;left&#8221; row_position=&#8221;default&#8221; row_position_tablet=&#8221;inherit&#8221; row_position_phone=&#8221;inherit&#8221; overflow=&#8221;visible&#8221; pointer_events=&#8221;all&#8221;][vc_column_inner column_padding=&#8221;padding-3-percent&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color=&#8221;#C1CFDC&#8221; background_color_opacity=&#8221;0.3&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/2&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;.vc_custom_1762706456431{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h5>Mechanism<\/h5>\n<p>Evaluation of the document architecture[\/vc_column_text][\/vc_column_inner][vc_column_inner column_padding=&#8221;padding-3-percent&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color=&#8221;#C1CFDC&#8221; background_color_opacity=&#8221;0.3&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/2&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;.vc_custom_1762700427152{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h5>Data objects<\/h5>\n[\/vc_column_text][nectar_badge display_tag=&#8221;body&#8221; badge_style=&#8221;default&#8221; bg_color_type=&#8221;global&#8221; color=&#8221;extra-color-gradient-2&#8243; text_color=&#8221;#ffffff&#8221; padding=&#8221;medium&#8221; border_radius=&#8221;3px&#8221; display=&#8221;inline&#8221; text=&#8221;JSON-LD&#8221; margin_right=&#8221;2&#8243; margin_bottom=&#8221;2&#8243;][nectar_badge display_tag=&#8221;body&#8221; badge_style=&#8221;default&#8221; bg_color_type=&#8221;global&#8221; color=&#8221;extra-color-gradient-2&#8243; text_color=&#8221;#ffffff&#8221; padding=&#8221;medium&#8221; border_radius=&#8221;3px&#8221; display=&#8221;inline&#8221; text=&#8221;Schema.org&#8221; margin_right=&#8221;2&#8243; margin_bottom=&#8221;2&#8243;][nectar_badge display_tag=&#8221;body&#8221; badge_style=&#8221;default&#8221; bg_color_type=&#8221;global&#8221; color=&#8221;extra-color-gradient-2&#8243; text_color=&#8221;#ffffff&#8221; padding=&#8221;medium&#8221; border_radius=&#8221;3px&#8221; display=&#8221;inline&#8221; text=&#8221;sameAs&#8221; margin_right=&#8221;2&#8243; margin_bottom=&#8221;2&#8243;][\/vc_column_inner][\/vc_row_inner][vc_row_inner column_margin=&#8221;5px&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; text_align=&#8221;left&#8221; row_position=&#8221;default&#8221; row_position_tablet=&#8221;inherit&#8221; row_position_phone=&#8221;inherit&#8221; overflow=&#8221;visible&#8221; pointer_events=&#8221;all&#8221;][vc_column_inner column_padding=&#8221;padding-3-percent&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color=&#8221;#C1CFDC&#8221; background_color_opacity=&#8221;0.3&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;.vc_custom_1762706529906{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h5>Mode of action<\/h5>\n<p>Strengthens evidential value and citation probability. Sources with stable anchors are given greater weight.[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; top_padding=&#8221;2%&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overflow=&#8221;visible&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_color=&#8221;#000000&#8243; shape_divider_position=&#8221;top&#8221; shape_divider_height=&#8221;5&#8243; bg_image_animation=&#8221;none&#8221; gradient_type=&#8221;default&#8221; shape_type=&#8221;straight_section&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; top_margin=&#8221;30&#8243; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_row_inner equal_height=&#8221;yes&#8221; content_placement=&#8221;bottom&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; text_align=&#8221;left&#8221; row_position=&#8221;default&#8221; row_position_tablet=&#8221;inherit&#8221; row_position_phone=&#8221;inherit&#8221; overflow=&#8221;visible&#8221; pointer_events=&#8221;all&#8221;][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; bottom_margin=&#8221;-10&#8243; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][nectar_icon icon_family=&#8221;fontawesome&#8221; icon_style=&#8221;default&#8221; icon_color_type=&#8221;color_scheme&#8221; icon_color=&#8221;Accent-Color&#8221; icon_padding=&#8221;0px&#8221; pointer_events=&#8221;all&#8221; icon_fontawesome=&#8221;fa fa-star&#8221; icon_size=&#8221;14&#8243; margin_right=&#8221;5&#8243;][nectar_icon icon_family=&#8221;fontawesome&#8221; icon_style=&#8221;default&#8221; icon_color_type=&#8221;color_scheme&#8221; icon_color=&#8221;Accent-Color&#8221; icon_padding=&#8221;0px&#8221; pointer_events=&#8221;all&#8221; icon_fontawesome=&#8221;fa fa-star&#8221; icon_size=&#8221;14&#8243; margin_right=&#8221;5&#8243;][nectar_icon icon_family=&#8221;fontawesome&#8221; icon_style=&#8221;default&#8221; icon_color_type=&#8221;color_scheme&#8221; icon_color=&#8221;Accent-Color&#8221; icon_padding=&#8221;0px&#8221; pointer_events=&#8221;all&#8221; icon_fontawesome=&#8221;fa fa-star&#8221; icon_size=&#8221;14&#8243; margin_right=&#8221;5&#8243;][nectar_icon icon_family=&#8221;fontawesome&#8221; icon_style=&#8221;default&#8221; icon_color_type=&#8221;color_scheme&#8221; icon_color=&#8221;Accent-Color&#8221; icon_padding=&#8221;0px&#8221; pointer_events=&#8221;all&#8221; icon_fontawesome=&#8221;fa fa-star-o&#8221; icon_size=&#8221;14&#8243; margin_right=&#8221;5&#8243;][nectar_icon icon_family=&#8221;fontawesome&#8221; icon_style=&#8221;default&#8221; icon_color_type=&#8221;color_scheme&#8221; icon_color=&#8221;Accent-Color&#8221; icon_padding=&#8221;0px&#8221; pointer_events=&#8221;all&#8221; icon_fontawesome=&#8221;fa fa-star-o&#8221; icon_size=&#8221;14&#8243;][vc_column_text css=&#8221;.vc_custom_1762706641641{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h3>Layer 5a: Response Planning<\/h3>\n[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][vc_row_inner equal_height=&#8221;yes&#8221; column_margin=&#8221;5px&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; text_align=&#8221;left&#8221; row_position=&#8221;default&#8221; row_position_tablet=&#8221;inherit&#8221; row_position_phone=&#8221;inherit&#8221; overflow=&#8221;visible&#8221; pointer_events=&#8221;all&#8221;][vc_column_inner column_padding=&#8221;padding-3-percent&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color=&#8221;#C1CFDC&#8221; background_color_opacity=&#8221;0.3&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/2&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;.vc_custom_1762706673717{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h5>Mechanism<\/h5>\n<p>Structuring the argumentation logic[\/vc_column_text][\/vc_column_inner][vc_column_inner column_padding=&#8221;padding-3-percent&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color=&#8221;#C1CFDC&#8221; background_color_opacity=&#8221;0.3&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/2&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;.vc_custom_1762700427152{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h5>Data objects<\/h5>\n[\/vc_column_text][nectar_badge display_tag=&#8221;body&#8221; badge_style=&#8221;default&#8221; bg_color_type=&#8221;global&#8221; color=&#8221;extra-color-gradient-2&#8243; text_color=&#8221;#ffffff&#8221; padding=&#8221;medium&#8221; border_radius=&#8221;3px&#8221; display=&#8221;inline&#8221; text=&#8221;isPartOf&#8221; margin_right=&#8221;2&#8243; margin_bottom=&#8221;2&#8243;][nectar_badge display_tag=&#8221;body&#8221; badge_style=&#8221;default&#8221; bg_color_type=&#8221;global&#8221; color=&#8221;extra-color-gradient-2&#8243; text_color=&#8221;#ffffff&#8221; padding=&#8221;medium&#8221; border_radius=&#8221;3px&#8221; display=&#8221;inline&#8221; text=&#8221;about&#8221; margin_right=&#8221;2&#8243; margin_bottom=&#8221;2&#8243;][nectar_badge display_tag=&#8221;body&#8221; badge_style=&#8221;default&#8221; bg_color_type=&#8221;global&#8221; color=&#8221;extra-color-gradient-2&#8243; text_color=&#8221;#ffffff&#8221; padding=&#8221;medium&#8221; border_radius=&#8221;3px&#8221; display=&#8221;inline&#8221; text=&#8221;mentions&#8221; margin_right=&#8221;2&#8243; margin_bottom=&#8221;2&#8243;][\/vc_column_inner][\/vc_row_inner][vc_row_inner column_margin=&#8221;5px&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; text_align=&#8221;left&#8221; row_position=&#8221;default&#8221; row_position_tablet=&#8221;inherit&#8221; row_position_phone=&#8221;inherit&#8221; overflow=&#8221;visible&#8221; pointer_events=&#8221;all&#8221;][vc_column_inner column_padding=&#8221;padding-3-percent&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color=&#8221;#C1CFDC&#8221; background_color_opacity=&#8221;0.3&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;.vc_custom_1762706726248{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h5>Mode of action<\/h5>\n<p>Influences outline structure: Well-connected entities are prioritized as central argumentation points.[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; top_padding=&#8221;2%&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overflow=&#8221;visible&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_color=&#8221;#000000&#8243; shape_divider_position=&#8221;top&#8221; shape_divider_height=&#8221;5&#8243; bg_image_animation=&#8221;none&#8221; gradient_type=&#8221;default&#8221; shape_type=&#8221;straight_section&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; top_margin=&#8221;30&#8243; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_row_inner equal_height=&#8221;yes&#8221; content_placement=&#8221;bottom&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; text_align=&#8221;left&#8221; row_position=&#8221;default&#8221; row_position_tablet=&#8221;inherit&#8221; row_position_phone=&#8221;inherit&#8221; overflow=&#8221;visible&#8221; pointer_events=&#8221;all&#8221;][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; bottom_margin=&#8221;-10&#8243; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][nectar_icon icon_family=&#8221;fontawesome&#8221; icon_style=&#8221;default&#8221; icon_color_type=&#8221;color_scheme&#8221; icon_color=&#8221;Accent-Color&#8221; icon_padding=&#8221;0px&#8221; pointer_events=&#8221;all&#8221; icon_fontawesome=&#8221;fa fa-star&#8221; icon_size=&#8221;14&#8243; margin_right=&#8221;5&#8243;][nectar_icon icon_family=&#8221;fontawesome&#8221; icon_style=&#8221;default&#8221; icon_color_type=&#8221;color_scheme&#8221; icon_color=&#8221;Accent-Color&#8221; icon_padding=&#8221;0px&#8221; pointer_events=&#8221;all&#8221; icon_fontawesome=&#8221;fa fa-star&#8221; icon_size=&#8221;14&#8243; margin_right=&#8221;5&#8243;][nectar_icon icon_family=&#8221;fontawesome&#8221; icon_style=&#8221;default&#8221; icon_color_type=&#8221;color_scheme&#8221; icon_color=&#8221;Accent-Color&#8221; icon_padding=&#8221;0px&#8221; pointer_events=&#8221;all&#8221; icon_fontawesome=&#8221;fa fa-star&#8221; icon_size=&#8221;14&#8243; margin_right=&#8221;5&#8243;][nectar_icon icon_family=&#8221;fontawesome&#8221; icon_style=&#8221;default&#8221; icon_color_type=&#8221;color_scheme&#8221; icon_color=&#8221;Accent-Color&#8221; icon_padding=&#8221;0px&#8221; pointer_events=&#8221;all&#8221; icon_fontawesome=&#8221;fa fa-star&#8221; icon_size=&#8221;14&#8243; margin_right=&#8221;5&#8243;][nectar_icon icon_family=&#8221;fontawesome&#8221; icon_style=&#8221;default&#8221; icon_color_type=&#8221;color_scheme&#8221; icon_color=&#8221;Accent-Color&#8221; icon_padding=&#8221;0px&#8221; pointer_events=&#8221;all&#8221; icon_fontawesome=&#8221;fa fa-star&#8221; icon_size=&#8221;14&#8243;][vc_column_text css=&#8221;.vc_custom_1762706753496{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h3>Layer 6: Reasoning<\/h3>\n[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][vc_row_inner equal_height=&#8221;yes&#8221; column_margin=&#8221;5px&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; text_align=&#8221;left&#8221; row_position=&#8221;default&#8221; row_position_tablet=&#8221;inherit&#8221; row_position_phone=&#8221;inherit&#8221; overflow=&#8221;visible&#8221; pointer_events=&#8221;all&#8221;][vc_column_inner column_padding=&#8221;padding-3-percent&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color=&#8221;#C1CFDC&#8221; background_color_opacity=&#8221;0.3&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/2&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;.vc_custom_1762706784394{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h5>Mechanism<\/h5>\n<p>Context integration across multiple sources[\/vc_column_text][\/vc_column_inner][vc_column_inner column_padding=&#8221;padding-3-percent&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color=&#8221;#C1CFDC&#8221; background_color_opacity=&#8221;0.3&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/2&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;.vc_custom_1762700427152{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h5>Data objects<\/h5>\n[\/vc_column_text][nectar_badge display_tag=&#8221;body&#8221; badge_style=&#8221;default&#8221; bg_color_type=&#8221;global&#8221; color=&#8221;extra-color-gradient-2&#8243; text_color=&#8221;#ffffff&#8221; padding=&#8221;medium&#8221; border_radius=&#8221;3px&#8221; display=&#8221;inline&#8221; text=&#8221;Schema.org Graph&#8221; margin_right=&#8221;2&#8243; margin_bottom=&#8221;2&#8243;][nectar_badge display_tag=&#8221;body&#8221; badge_style=&#8221;default&#8221; bg_color_type=&#8221;global&#8221; color=&#8221;extra-color-gradient-2&#8243; text_color=&#8221;#ffffff&#8221; padding=&#8221;medium&#8221; border_radius=&#8221;3px&#8221; display=&#8221;inline&#8221; text=&#8221;Q-IDs&#8221; margin_right=&#8221;2&#8243; margin_bottom=&#8221;2&#8243;][nectar_badge display_tag=&#8221;body&#8221; badge_style=&#8221;default&#8221; bg_color_type=&#8221;global&#8221; color=&#8221;extra-color-gradient-2&#8243; text_color=&#8221;#ffffff&#8221; padding=&#8221;medium&#8221; border_radius=&#8221;3px&#8221; display=&#8221;inline&#8221; text=&#8221;Standard IDs&#8221; margin_right=&#8221;2&#8243; margin_bottom=&#8221;2&#8243;][\/vc_column_inner][\/vc_row_inner][vc_row_inner column_margin=&#8221;5px&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; text_align=&#8221;left&#8221; row_position=&#8221;default&#8221; row_position_tablet=&#8221;inherit&#8221; row_position_phone=&#8221;inherit&#8221; overflow=&#8221;visible&#8221; pointer_events=&#8221;all&#8221;][vc_column_inner column_padding=&#8221;padding-3-percent&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color=&#8221;#C1CFDC&#8221; background_color_opacity=&#8221;0.3&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;.vc_custom_1762707167339{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h5>Mode of action<\/h5>\n<p>Increases chance of RECOMMENDATION instead of mere mention. Consistent data acts as a &#8220;truth anchor&#8221;.[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; top_padding=&#8221;2%&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overflow=&#8221;visible&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_color=&#8221;#000000&#8243; shape_divider_position=&#8221;top&#8221; shape_divider_height=&#8221;5&#8243; bg_image_animation=&#8221;none&#8221; gradient_type=&#8221;default&#8221; shape_type=&#8221;straight_section&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; top_margin=&#8221;30&#8243; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_row_inner equal_height=&#8221;yes&#8221; content_placement=&#8221;bottom&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; text_align=&#8221;left&#8221; row_position=&#8221;default&#8221; row_position_tablet=&#8221;inherit&#8221; row_position_phone=&#8221;inherit&#8221; overflow=&#8221;visible&#8221; pointer_events=&#8221;all&#8221;][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; bottom_margin=&#8221;-10&#8243; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][nectar_icon icon_family=&#8221;fontawesome&#8221; icon_style=&#8221;default&#8221; icon_color_type=&#8221;color_scheme&#8221; icon_color=&#8221;Accent-Color&#8221; icon_padding=&#8221;0px&#8221; pointer_events=&#8221;all&#8221; icon_fontawesome=&#8221;fa fa-star&#8221; icon_size=&#8221;14&#8243; margin_right=&#8221;5&#8243;][nectar_icon icon_family=&#8221;fontawesome&#8221; icon_style=&#8221;default&#8221; icon_color_type=&#8221;color_scheme&#8221; icon_color=&#8221;Accent-Color&#8221; icon_padding=&#8221;0px&#8221; pointer_events=&#8221;all&#8221; icon_fontawesome=&#8221;fa fa-star&#8221; icon_size=&#8221;14&#8243; margin_right=&#8221;5&#8243;][nectar_icon icon_family=&#8221;fontawesome&#8221; icon_style=&#8221;default&#8221; icon_color_type=&#8221;color_scheme&#8221; icon_color=&#8221;Accent-Color&#8221; icon_padding=&#8221;0px&#8221; pointer_events=&#8221;all&#8221; icon_fontawesome=&#8221;fa fa-star&#8221; icon_size=&#8221;14&#8243; margin_right=&#8221;5&#8243;][nectar_icon icon_family=&#8221;fontawesome&#8221; icon_style=&#8221;default&#8221; icon_color_type=&#8221;color_scheme&#8221; icon_color=&#8221;Accent-Color&#8221; icon_padding=&#8221;0px&#8221; pointer_events=&#8221;all&#8221; icon_fontawesome=&#8221;fa fa-star-o&#8221; icon_size=&#8221;14&#8243; margin_right=&#8221;5&#8243;][nectar_icon icon_family=&#8221;fontawesome&#8221; icon_style=&#8221;default&#8221; icon_color_type=&#8221;color_scheme&#8221; icon_color=&#8221;Accent-Color&#8221; icon_padding=&#8221;0px&#8221; pointer_events=&#8221;all&#8221; icon_fontawesome=&#8221;fa fa-star-o&#8221; icon_size=&#8221;14&#8243;][vc_column_text css=&#8221;.vc_custom_1762706855975{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h3>Layer 7: Final Response<\/h3>\n[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][vc_row_inner equal_height=&#8221;yes&#8221; column_margin=&#8221;5px&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; text_align=&#8221;left&#8221; row_position=&#8221;default&#8221; row_position_tablet=&#8221;inherit&#8221; row_position_phone=&#8221;inherit&#8221; overflow=&#8221;visible&#8221; pointer_events=&#8221;all&#8221;][vc_column_inner column_padding=&#8221;padding-3-percent&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color=&#8221;#C1CFDC&#8221; background_color_opacity=&#8221;0.3&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/2&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;.vc_custom_1762706890019{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h5>Mechanism<\/h5>\n<p>Citation integration &amp; transparency[\/vc_column_text][\/vc_column_inner][vc_column_inner column_padding=&#8221;padding-3-percent&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color=&#8221;#C1CFDC&#8221; background_color_opacity=&#8221;0.3&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/2&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;.vc_custom_1762700427152{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h5>Data objects<\/h5>\n[\/vc_column_text][nectar_badge display_tag=&#8221;body&#8221; badge_style=&#8221;default&#8221; bg_color_type=&#8221;global&#8221; color=&#8221;extra-color-gradient-2&#8243; text_color=&#8221;#ffffff&#8221; padding=&#8221;medium&#8221; border_radius=&#8221;3px&#8221; display=&#8221;inline&#8221; text=&#8221;url&#8221; margin_right=&#8221;2&#8243; margin_bottom=&#8221;2&#8243;][nectar_badge display_tag=&#8221;body&#8221; badge_style=&#8221;default&#8221; bg_color_type=&#8221;global&#8221; color=&#8221;extra-color-gradient-2&#8243; text_color=&#8221;#ffffff&#8221; padding=&#8221;medium&#8221; border_radius=&#8221;3px&#8221; display=&#8221;inline&#8221; text=&#8221;name&#8221; margin_right=&#8221;2&#8243; margin_bottom=&#8221;2&#8243;][nectar_badge display_tag=&#8221;body&#8221; badge_style=&#8221;default&#8221; bg_color_type=&#8221;global&#8221; color=&#8221;extra-color-gradient-2&#8243; text_color=&#8221;#ffffff&#8221; padding=&#8221;medium&#8221; border_radius=&#8221;3px&#8221; display=&#8221;inline&#8221; text=&#8221;publisher&#8221; margin_right=&#8221;2&#8243; margin_bottom=&#8221;2&#8243;][nectar_badge display_tag=&#8221;body&#8221; badge_style=&#8221;default&#8221; bg_color_type=&#8221;global&#8221; color=&#8221;extra-color-gradient-2&#8243; text_color=&#8221;#ffffff&#8221; padding=&#8221;medium&#8221; border_radius=&#8221;3px&#8221; display=&#8221;inline&#8221; text=&#8221;datePublished&#8221; margin_right=&#8221;2&#8243; margin_bottom=&#8221;2&#8243;][\/vc_column_inner][\/vc_row_inner][vc_row_inner column_margin=&#8221;5px&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; text_align=&#8221;left&#8221; row_position=&#8221;default&#8221; row_position_tablet=&#8221;inherit&#8221; row_position_phone=&#8221;inherit&#8221; overflow=&#8221;visible&#8221; pointer_events=&#8221;all&#8221;][vc_column_inner column_padding=&#8221;padding-3-percent&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color=&#8221;#C1CFDC&#8221; background_color_opacity=&#8221;0.3&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;.vc_custom_1762706903331{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h5>Mode of action<\/h5>\n<p>Enables explicit source citation instead of vague references. Structured metadata for attribution.[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overflow=&#8221;visible&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_position=&#8221;bottom&#8221; bg_image_animation=&#8221;none&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][vc_row_inner column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; text_align=&#8221;left&#8221; row_position=&#8221;default&#8221; row_position_tablet=&#8221;inherit&#8221; row_position_phone=&#8221;inherit&#8221; overflow=&#8221;visible&#8221; pointer_events=&#8221;all&#8221;][vc_column_inner top_padding_desktop=&#8221;60&#8243; bottom_padding_desktop=&#8221;60&#8243; top_margin=&#8221;60&#8243; bottom_margin=&#8221;60&#8243; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;advanced&#8221; border_top_desktop=&#8221;4&#8243; border_bottom_desktop=&#8221;4&#8243; column_border_color=&#8221;#000000&#8243; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;advanced&#8221; gradient_type=&#8221;default&#8221;][nectar_single_testimonial testimonial_style=&#8221;small_modern&#8221; image=&#8221;4361&#8243; color=&#8221;Default&#8221; quote=&#8221;Structured data not only affects retrieval (phase 2-3), but above all entity recognition (4), evidence weighting (5), response planning (5a) and reasoning (6). This distinguishes semantic data architecture from superficial content marketing.&#8221; name=&#8221;Norbert Kathriner&#8221;][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; top_padding=&#8221;2%&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overflow=&#8221;visible&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_color=&#8221;#000000&#8243; shape_divider_position=&#8221;top&#8221; shape_divider_height=&#8221;5&#8243; bg_image_animation=&#8221;none&#8221; gradient_type=&#8221;default&#8221; shape_type=&#8221;straight_section&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; top_margin=&#8221;30&#8243; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;.vc_custom_1762692436453{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h2><span class=\"ez-toc-section\" id=\"The_mechanics_in_detail_All_steps_from_the_request_to_the_response\"><\/span>The mechanics in detail: All steps from the request to the response<span class=\"ez-toc-section-end\"><\/span><\/h2>\n[\/vc_column_text][vc_column_text css=&#8221;.vc_custom_1762692461744{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]<strong>What was previously considered a black box can now be reconstructed through systematic analysis.<\/strong>[\/vc_column_text][vc_column_text css=&#8221;.vc_custom_1762692571028{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]The following descriptions are based on 14 months of field research, evaluation of over 500 prompt-response pairs across multiple LLM platforms, and comparison with current RAG and entity linking research. They do not show how a specific model is programmed internally &#8211; that remains proprietary. But they do show the logical steps a system has to go through to get from a natural language query to a structured, evidence-based response.<\/p>\n<p>This granularity is not an academic end in itself. It is the basis for the points at which structured data is effective &#8211; and therefore the prerequisite for strategic decisions: <strong>Where is it worth investing in machine-readable evidence? Which Schema.org properties have a measurable impact? When is content marketing enough, when is semantic architecture needed? <\/strong><\/p>\n<p>The digital communications boutique is conducting this fundamental research not just out of academic interest, but out of necessity: <strong>if you want to understand how brands will retain control of their content in an LLM-dominated future, you need to understand how these systems make decisions.<\/strong> What follows is not speculation &#8211; it is the most detailed, publicly available process description of LLM response formation to date.[\/vc_column_text][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; top_padding=&#8221;2%&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overflow=&#8221;visible&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_color=&#8221;#000000&#8243; shape_divider_position=&#8221;top&#8221; shape_divider_height=&#8221;5&#8243; bg_image_animation=&#8221;none&#8221; gradient_type=&#8221;default&#8221; shape_type=&#8221;straight_section&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; top_margin=&#8221;30&#8243; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;.vc_custom_1762715038490{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h2><span class=\"ez-toc-section\" id=\"Part_I_%E2%80%93_Case_L_answer_based_on_learning_data\"><\/span>Part I &#8211; Case L (answer based on learning data)<span class=\"ez-toc-section-end\"><\/span><\/h2>\n[\/vc_column_text][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; top_padding=&#8221;4%&#8221; bottom_padding=&#8221;2%&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overflow=&#8221;visible&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_color=&#8221;#000000&#8243; shape_divider_position=&#8221;top&#8221; shape_divider_height=&#8221;5&#8243; bg_image_animation=&#8221;none&#8221; gradient_type=&#8221;default&#8221; shape_type=&#8221;straight_section&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][nectar_badge display_tag=&#8221;body&#8221; badge_style=&#8221;default&#8221; bg_color_type=&#8221;global&#8221; color=&#8221;extra-color-gradient-2&#8243; text_color=&#8221;#ffffff&#8221; padding=&#8221;medium&#8221; border_radius=&#8221;3px&#8221; display=&#8221;block&#8221; text=&#8221;Phase 0&#8243;][vc_column_text css=&#8221;.vc_custom_1762714753776{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h3>Prompt Input \/ Intent Parsing<\/h3>\n[\/vc_column_text][divider line_type=&#8221;Full Width Line&#8221; line_thickness=&#8221;4&#8243; divider_color=&#8221;default&#8221; custom_height=&#8221;30&#8243;][vc_column_text css=&#8221;.vc_custom_1762623036738{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]The LLM process steps[\/vc_column_text][nectar_icon_list color=&#8221;Accent-Color&#8221; direction=&#8221;horizontal&#8221; columns=&#8221;2&#8243; icon_size=&#8221;small&#8221; icon_style=&#8221;border&#8221;][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055355995-3&#8243; tab_id=&#8221;1763055355996-7&#8243; header=&#8221;Prompt reception&#8221; text=&#8221;Processing the user input&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055356008-8&#8243; tab_id=&#8221;1763055356008-1&#8243; header=&#8221;Tokenization&#8221; text=&#8221;Breakdown into processable units.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055356021-8&#8243; tab_id=&#8221;1763055356021-1&#8243; header=&#8221;Semantic analysis&#8221; text=&#8221;Recognition of intent, topic, entities, relations, time reference.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055356034-9&#8243; tab_id=&#8221;1763055356035-4&#8243; header=&#8221;Intent vector creation&#8221; text=&#8221;Abstract representation of the request as a semantic vector.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055356049-0&#8243; tab_id=&#8221;1763055356049-0&#8243; header=&#8221;Complexity assessment&#8221; text=&#8221;Assessment of response type, document requirements and potential timeliness.&#8221;][\/nectar_icon_list_item][\/nectar_icon_list][divider line_type=&#8221;Full Width Line&#8221; line_thickness=&#8221;4&#8243; divider_color=&#8221;default&#8221; custom_height=&#8221;30&#8243;][vc_row_inner column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; bottom_padding=&#8221;2%&#8221; text_align=&#8221;left&#8221; row_position=&#8221;default&#8221; row_position_tablet=&#8221;inherit&#8221; row_position_phone=&#8221;inherit&#8221; overflow=&#8221;visible&#8221; pointer_events=&#8221;all&#8221;][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; bottom_margin=&#8221;30&#8243; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;.vc_custom_1762620685271{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]Before a model &#8220;knows&#8221; anything, it has to understand what is actually required. Phase 0 is therefore not a content-related step, but an epistemic one: a human formulation becomes a machine-readable intention. Technically, this means breaking down the text into units, recognizing fields of meaning and encoding these fields as an intent vector. This vector is the working basis for all further decisions: Do we stay in the internal knowledge space (Case L), or do we open the search space (Case L+O)?<\/p>\n<p>Important: This phase does not decide anything about truth content. It merely determines relevance paths &#8211; i.e. which topics, entities and relations are even possible. At the same time, the model assesses the complexity: Is the question purely definitional? Is it time-related? Does it require evidence? This assessment results in an initial confidence value, which later serves as a switch. The clearer the intent, the lower the friction losses in all subsequent phases and the less susceptibility to prompt sensitivity.<\/p>\n<p>For brands, this means that linguistic and semantic clarity already pays off here. If you use consistent terms, stable entities and unambiguous designations in your own communication, you increase the probability that the model will process the question in the correct semantic neighborhood. Phase 0 is therefore the place where machine readability begins as a prerequisite for everything else.[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; top_padding=&#8221;2%&#8221; bottom_padding=&#8221;2%&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overflow=&#8221;visible&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_color=&#8221;#000000&#8243; shape_divider_position=&#8221;top&#8221; shape_divider_height=&#8221;5&#8243; bg_image_animation=&#8221;none&#8221; gradient_type=&#8221;default&#8221; shape_type=&#8221;straight_section&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][nectar_badge display_tag=&#8221;body&#8221; badge_style=&#8221;default&#8221; bg_color_type=&#8221;global&#8221; color=&#8221;extra-color-gradient-2&#8243; text_color=&#8221;#ffffff&#8221; padding=&#8221;medium&#8221; border_radius=&#8221;3px&#8221; display=&#8221;block&#8221; text=&#8221;Phase 1&#8243;][vc_column_text css=&#8221;.vc_custom_1762709483187{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h3>Internal Knowledge Retrieval<\/h3>\n[\/vc_column_text][divider line_type=&#8221;Full Width Line&#8221; line_thickness=&#8221;4&#8243; divider_color=&#8221;default&#8221; custom_height=&#8221;30&#8243;][vc_column_text css=&#8221;.vc_custom_1762623036738{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]The LLM process steps[\/vc_column_text][nectar_icon_list color=&#8221;Accent-Color&#8221; direction=&#8221;horizontal&#8221; columns=&#8221;2&#8243; icon_size=&#8221;small&#8221; icon_style=&#8221;border&#8221;][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055356181-0&#8243; tab_id=&#8221;1763055356181-7&#8243; header=&#8221;Parametric knowledge retrieval&#8221; text=&#8221;Access to trained weights.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055356190-6&#8243; tab_id=&#8221;1763055356191-1&#8243; header=&#8221;Concept activation&#8221; text=&#8221;Relevant knowledge patterns are activated.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055356204-4&#8243; tab_id=&#8221;1763055356204-8&#8243; header=&#8221;Sample extraction&#8221; text=&#8221;Recall of memorized structures and contexts.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055356218-1&#8243; tab_id=&#8221;1763055356219-2&#8243; header=&#8221;Contextual filtering&#8221; text=&#8221;Limitation to relevant areas of knowledge.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055356232-0&#8243; tab_id=&#8221;1763055356232-5&#8243; header=&#8221;Confidence score calculation&#8221; text=&#8221;Assessment of the internal strength of evidence.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055356246-2&#8243; tab_id=&#8221;1763055356247-7&#8243; header=&#8221;Sufficiency check&#8221; text=&#8221;Check whether the internal evidence is sufficient to remain in Case L.&#8221;][\/nectar_icon_list_item][\/nectar_icon_list][divider line_type=&#8221;Full Width Line&#8221; line_thickness=&#8221;4&#8243; divider_color=&#8221;default&#8221; custom_height=&#8221;30&#8243;][vc_row_inner column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; bottom_padding=&#8221;2%&#8221; text_align=&#8221;left&#8221; row_position=&#8221;default&#8221; row_position_tablet=&#8221;inherit&#8221; row_position_phone=&#8221;inherit&#8221; overflow=&#8221;visible&#8221; pointer_events=&#8221;all&#8221;][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; bottom_margin=&#8221;30&#8243; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;.vc_custom_1762709517794{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]Before a language model looks outwards, it first looks within itself. This phase is the core of every purely internal answer (Case L): Here it is checked whether the existing world knowledge is sufficient to answer the query conclusively.<\/p>\n<p>Technically speaking, the model accesses its <b>parametric knowledge base<\/b> &#8211; a condensed matrix of weights that were learned during training. These parameters do not store facts in the classical sense, but rather <b>probability spaces of meaning<\/b>. When a prompt activates a certain topic, the model does not react by looking it up, but by <b>activating concept patterns<\/b>: links between concepts, relations and typical argumentation chains.<\/p>\n<p>This phase determines how &#8220;stable&#8221; the model thinks. It evaluates the <b>density of evidence<\/b> in its own memory, filters out noise and forms an internal confidence measure &#8211; the <i>confidence score<\/i>. If this is high enough, the model remains in its closed system (Case L). If it is low or contradictory, this triggers the transition to hybrid mode (Case L+O).<\/p>\n<p>This phase is crucial for brands because it determines <b>whether a brand even exists in the collective memory of the machine<\/b>. Anyone who does not appear here will hardly be mentioned organically, even with correct online research. This is why internal visibility &#8211; i.e. trainable evidence &#8211; is the basis for any subsequent AI visibility.[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; top_padding=&#8221;2%&#8221; bottom_padding=&#8221;2%&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overflow=&#8221;visible&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_color=&#8221;#000000&#8243; shape_divider_position=&#8221;top&#8221; shape_divider_height=&#8221;5&#8243; bg_image_animation=&#8221;none&#8221; gradient_type=&#8221;default&#8221; shape_type=&#8221;straight_section&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][nectar_badge display_tag=&#8221;body&#8221; badge_style=&#8221;default&#8221; bg_color_type=&#8221;global&#8221; color=&#8221;extra-color-gradient-2&#8243; text_color=&#8221;#ffffff&#8221; padding=&#8221;medium&#8221; border_radius=&#8221;3px&#8221; display=&#8221;inline&#8221; text=&#8221;Phase 2&#8243; margin_right=&#8221;2&#8243;][nectar_badge display_tag=&#8221;body&#8221; badge_style=&#8221;default&#8221; bg_color_type=&#8221;global&#8221; color=&#8221;extra-color-gradient-2&#8243; text_color=&#8221;#ffffff&#8221; padding=&#8221;medium&#8221; border_radius=&#8221;3px&#8221; display=&#8221;inline&#8221; text=&#8221;Phase 3&#8243; margin_right=&#8221;2&#8243;][vc_column_text css=&#8221;.vc_custom_1762713007629{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h3>Not applicable \/ skipped in Case L<\/h3>\n<p>Phase 2 &#8211; Fan-Out (only Case L+O)<br \/>\nPhase 3 &#8211; Evidence Extraction (Case L+O only)[\/vc_column_text][divider line_type=&#8221;Full Width Line&#8221; line_thickness=&#8221;4&#8243; divider_color=&#8221;default&#8221; custom_height=&#8221;30&#8243;][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; top_padding=&#8221;2%&#8221; bottom_padding=&#8221;2%&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overflow=&#8221;visible&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_color=&#8221;#000000&#8243; shape_divider_position=&#8221;top&#8221; shape_divider_height=&#8221;5&#8243; bg_image_animation=&#8221;none&#8221; gradient_type=&#8221;default&#8221; shape_type=&#8221;straight_section&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][nectar_badge display_tag=&#8221;body&#8221; badge_style=&#8221;default&#8221; bg_color_type=&#8221;global&#8221; color=&#8221;extra-color-gradient-2&#8243; text_color=&#8221;#ffffff&#8221; padding=&#8221;medium&#8221; border_radius=&#8221;3px&#8221; display=&#8221;block&#8221; text=&#8221;Phase 4&#8243;][vc_column_text css=&#8221;.vc_custom_1762715229790{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h3>Entity Recognition (trained patterns)<\/h3>\n[\/vc_column_text][divider line_type=&#8221;Full Width Line&#8221; line_thickness=&#8221;4&#8243; divider_color=&#8221;default&#8221; custom_height=&#8221;30&#8243;][vc_column_text css=&#8221;.vc_custom_1762623036738{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]The LLM process steps[\/vc_column_text][nectar_icon_list color=&#8221;Accent-Color&#8221; direction=&#8221;horizontal&#8221; columns=&#8221;2&#8243; icon_size=&#8221;small&#8221; icon_style=&#8221;border&#8221;][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055356523-4&#8243; tab_id=&#8221;1763055356523-1&#8243; header=&#8221;Entity recognition&#8221; text=&#8221;Identification of relevant concepts, objects or organizations.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055356532-9&#8243; tab_id=&#8221;1763055356532-4&#8243; header=&#8221;Trained entity linking&#8221; text=&#8221;Assignment to learned entities in model memory.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055356546-0&#8243; tab_id=&#8221;1763055356546-8&#8243; header=&#8221;Q-ID matching (internal)&#8221; text=&#8221;Comparison with stored identifiers from the training.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055356552-10&#8243; tab_id=&#8221;1763055356553-7&#8243; header=&#8221;Relationship mapping&#8221; text=&#8221;Recognition and linking of semantic relations between entities.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055356566-6&#8243; tab_id=&#8221;1763055356566-2&#8243; header=&#8221;Contextualization&#8221; text=&#8221;Integration into the overall thematic context of the prompt.&#8221;][\/nectar_icon_list_item][\/nectar_icon_list][divider line_type=&#8221;Full Width Line&#8221; line_thickness=&#8221;4&#8243; divider_color=&#8221;default&#8221; custom_height=&#8221;30&#8243;][vc_row_inner column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; bottom_padding=&#8221;2%&#8221; text_align=&#8221;left&#8221; row_position=&#8221;default&#8221; row_position_tablet=&#8221;inherit&#8221; row_position_phone=&#8221;inherit&#8221; overflow=&#8221;visible&#8221; pointer_events=&#8221;all&#8221;][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; bottom_margin=&#8221;30&#8243; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;.vc_custom_1762709869250{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]When a language model &#8220;knows&#8221; something, it rarely recognizes concepts &#8211; it understands via entities. This phase translates fuzzy human thinking into a structured, machine-readable form: words become nodes, connections become relations.<\/p>\n<p>The model identifies which concepts, organizations, places or people appear in the prompt and checks whether they already exist as trained entities in the internal knowledge space. Each of these entities has a kind of semantic fingerprint: a combination of name, context and typical attributes. The internal comparison is made with stored patterns or &#8211; in a figurative sense &#8211; with known Q-IDs from the training knowledge.<\/p>\n<p>The aim of this phase is not just recognition, but clarity. The model can only formulate coherent answers if it knows for sure which Allianz or Zurich is meant. To do this, it links entities with each other (&#8220;Allianz SE&#8221; &#8211; &#8220;insurance&#8221; &#8211; &#8220;Germany&#8221;) and builds a local graph of meaning. Only this graph turns mere word association into semantically comprehensible thinking.<\/p>\n<p>For brands, this means that this is where it is decided whether they appear as a clear entity or are lost in the noise of meaning. Only those that are stable in terms of content, formally consistent and referenced multiple times are recognized as independent nodes &#8211; and can later exist in generative responses as a &#8220;source&#8221; or &#8220;example&#8221;.[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; top_padding=&#8221;2%&#8221; bottom_padding=&#8221;2%&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overflow=&#8221;visible&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_color=&#8221;#000000&#8243; shape_divider_position=&#8221;top&#8221; shape_divider_height=&#8221;5&#8243; bg_image_animation=&#8221;none&#8221; gradient_type=&#8221;default&#8221; shape_type=&#8221;straight_section&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][nectar_badge display_tag=&#8221;body&#8221; badge_style=&#8221;default&#8221; bg_color_type=&#8221;global&#8221; color=&#8221;extra-color-gradient-2&#8243; text_color=&#8221;#ffffff&#8221; padding=&#8221;medium&#8221; border_radius=&#8221;3px&#8221; display=&#8221;block&#8221; text=&#8221;Phase 5&#8243;][vc_column_text css=&#8221;.vc_custom_1762715546969{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h3>Evidence weighting (internal frequency &amp; coherence)<\/h3>\n[\/vc_column_text][divider line_type=&#8221;Full Width Line&#8221; line_thickness=&#8221;4&#8243; divider_color=&#8221;default&#8221; custom_height=&#8221;30&#8243;][vc_column_text css=&#8221;.vc_custom_1762623036738{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]The LLM process steps[\/vc_column_text][nectar_icon_list color=&#8221;Accent-Color&#8221; direction=&#8221;horizontal&#8221; columns=&#8221;2&#8243; icon_size=&#8221;small&#8221; icon_style=&#8221;border&#8221;][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055356806-6&#8243; tab_id=&#8221;1763055356806-0&#8243; header=&#8221;Frequency analysis&#8221; text=&#8221;Determination of the frequency of activated patterns in the training room.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055356816-0&#8243; tab_id=&#8221;1763055356816-8&#8243; header=&#8221;Coherence check&#8221; text=&#8221;Verification of internal consistency.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055356831-6&#8243; tab_id=&#8221;1763055356831-5&#8243; header=&#8221;Prioritization&#8221; text=&#8221;Weighting of the most stable patterns and relationships.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055356845-9&#8243; tab_id=&#8221;1763055356846-8&#8243; header=&#8221;Evidence score calculation&#8221; text=&#8221;Determination of the probative value of each sample.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055356859-9&#8243; tab_id=&#8221;1763055356860-9&#8243; header=&#8221;Preparation for phase 6&#8243; text=&#8221;Passing on the weighted evidence for reasoning.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055356865-10&#8243; tab_id=&#8221;1763055356866-2&#8243; header=&#8221;Narrative structuring&#8221; text=&#8221;Building a logical chain of reasoning as the basis for the answer.&#8221;][\/nectar_icon_list_item][\/nectar_icon_list][divider line_type=&#8221;Full Width Line&#8221; line_thickness=&#8221;4&#8243; divider_color=&#8221;default&#8221; custom_height=&#8221;30&#8243;][vc_row_inner column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; bottom_padding=&#8221;2%&#8221; text_align=&#8221;left&#8221; row_position=&#8221;default&#8221; row_position_tablet=&#8221;inherit&#8221; row_position_phone=&#8221;inherit&#8221; overflow=&#8221;visible&#8221; pointer_events=&#8221;all&#8221;][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; bottom_margin=&#8221;30&#8243; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;.vc_custom_1762715408406{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]Once the model has identified the relevant entities and mapped their relationships, the key question is: <strong>Which of this internal information is robust enough to become part of the answer?<\/strong><\/p>\n<p>In this phase, the model evaluates the <strong>strength of evidence of its own knowledge stored in the training<\/strong>. There are no external sources &#8211; the evaluation takes place exclusively in the <strong>parametric space<\/strong> of the model.<br \/>\nEach connection between terms and concepts carries a statistical weight that is derived from the frequency and stability in the training data.<\/p>\n<p>Two dimensions determine this internal evidence assessment:<\/p>\n<ol>\n<li><strong>Frequency<\/strong> &#8211; How often does a pattern occur in similar contexts? Frequent, consistent links are given a higher internal weight.<\/li>\n<li><strong>Coherence<\/strong> &#8211; How consistent is the relationship within the semantic field? Inconsistencies or competing meanings reduce trust.<\/li>\n<\/ol>\n<p>Both factors are used to create an <strong>internal evidence score<\/strong>, which indicates which concepts serve as a viable basis for the answer. This internal weighting replaces the external evidence process in Case L &#8211; it is the machine equivalent of a plausibility check.<\/p>\n<p>This phase is crucial for brands because it shows <strong>how strongly a brand is already anchored in the collective memory of the machine<\/strong>. Only frequently linked, semantically clear brand references generate enough internal evidence to appear in responses without additional online research.[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; top_padding=&#8221;2%&#8221; bottom_padding=&#8221;2%&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overflow=&#8221;visible&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_color=&#8221;#000000&#8243; shape_divider_position=&#8221;top&#8221; shape_divider_height=&#8221;5&#8243; bg_image_animation=&#8221;none&#8221; gradient_type=&#8221;default&#8221; shape_type=&#8221;straight_section&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][nectar_badge display_tag=&#8221;body&#8221; badge_style=&#8221;default&#8221; bg_color_type=&#8221;global&#8221; color=&#8221;extra-color-gradient-2&#8243; text_color=&#8221;#ffffff&#8221; padding=&#8221;medium&#8221; border_radius=&#8221;3px&#8221; display=&#8221;block&#8221; text=&#8221;Phase 6&#8243;][vc_column_text css=&#8221;.vc_custom_1762714859279{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h3>Reasoning (parametric synthesis)<\/h3>\n[\/vc_column_text][divider line_type=&#8221;Full Width Line&#8221; line_thickness=&#8221;4&#8243; divider_color=&#8221;default&#8221; custom_height=&#8221;30&#8243;][vc_column_text css=&#8221;.vc_custom_1762623036738{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]The LLM process steps[\/vc_column_text][nectar_icon_list color=&#8221;Accent-Color&#8221; direction=&#8221;horizontal&#8221; columns=&#8221;2&#8243; icon_size=&#8221;small&#8221; icon_style=&#8221;border&#8221;][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055357065-4&#8243; tab_id=&#8221;1763055357066-8&#8243; header=&#8221;Knowledge consolidation&#8221; text=&#8221;Merging of all relevant patterns and entities.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055357076-10&#8243; tab_id=&#8221;1763055357077-9&#8243; header=&#8221;Logical inference&#8221; text=&#8221;Derivation of conclusions on the basis of internal relations.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055357083-8&#8243; tab_id=&#8221;1763055357083-10&#8243; header=&#8221;Coherence check&#8221; text=&#8221;Check for consistency and semantic stability.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055357099-1&#8243; tab_id=&#8221;1763055357100-10&#8243; header=&#8221;Conceptual integration&#8221; text=&#8221;Combining different levels of knowledge into an overall structure.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055357115-7&#8243; tab_id=&#8221;1763055357116-6&#8243; header=&#8221;Frequency weighting&#8221; text=&#8221;Prioritization of frequently and consistently occurring patterns.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055357131-1&#8243; tab_id=&#8221;1763055357131-2&#8243; header=&#8221;Narrative structuring&#8221; text=&#8221;Building a logical chain of argumentation as a basis for linguistic formulation.&#8221;][\/nectar_icon_list_item][\/nectar_icon_list][divider line_type=&#8221;Full Width Line&#8221; line_thickness=&#8221;4&#8243; divider_color=&#8221;default&#8221; custom_height=&#8221;30&#8243;][vc_row_inner column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; bottom_padding=&#8221;2%&#8221; text_align=&#8221;left&#8221; row_position=&#8221;default&#8221; row_position_tablet=&#8221;inherit&#8221; row_position_phone=&#8221;inherit&#8221; overflow=&#8221;visible&#8221; pointer_events=&#8221;all&#8221;][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; bottom_margin=&#8221;30&#8243; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;.vc_custom_1762715596406{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]Once the model has evaluated its internal evidence, the actual <strong>synthesis phase<\/strong> begins &#8211; what we would call thinking in humans. Now the weighted patterns, entities and relationships are brought together to form a consistent structure of meaning.<\/p>\n<p>The model checks which concepts fit together, which support each other and which contradict each other. It draws <strong>logical and semantic conclusions<\/strong>, consolidates similar patterns and discards inconsistent relations. This process is similar to the mental comparison of several memories: A plausible explanation emerges from scattered elements of knowledge that is internally stable enough to be expressed linguistically.<\/p>\n<p>The prioritization follows the <strong>frequency weighting<\/strong> from phase 5: Frequently occurring, consistent patterns are treated as &#8220;main axes&#8221; of meaning, rare or weak patterns as supplementary marginal information.<br \/>\nThe goal is <strong>coherence<\/strong> &#8211; an answer that is logically coherent, semantically harmonious and statistically plausible.<\/p>\n<p>For brands, this phase means that this is where it is decided <strong>whether their presence in the model is considered a stable knowledge unit<\/strong>. A brand whose content appears consistently across many contexts is perceived as a reliable node in this internal space of meaning &#8211; and can therefore also appear in answers without external sources.[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; top_padding=&#8221;2%&#8221; bottom_padding=&#8221;2%&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overflow=&#8221;visible&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_color=&#8221;#000000&#8243; shape_divider_position=&#8221;top&#8221; shape_divider_height=&#8221;5&#8243; bg_image_animation=&#8221;none&#8221; gradient_type=&#8221;default&#8221; shape_type=&#8221;straight_section&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][nectar_badge display_tag=&#8221;body&#8221; badge_style=&#8221;default&#8221; bg_color_type=&#8221;global&#8221; color=&#8221;extra-color-gradient-2&#8243; text_color=&#8221;#ffffff&#8221; padding=&#8221;medium&#8221; border_radius=&#8221;3px&#8221; display=&#8221;block&#8221; text=&#8221;Phase 7&#8243;][vc_column_text css=&#8221;.vc_custom_1762714894832{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h3>Final Response Construction<\/h3>\n[\/vc_column_text][divider line_type=&#8221;Full Width Line&#8221; line_thickness=&#8221;4&#8243; divider_color=&#8221;default&#8221; custom_height=&#8221;30&#8243;][vc_column_text css=&#8221;.vc_custom_1762623036738{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]The LLM process steps[\/vc_column_text][nectar_icon_list color=&#8221;Accent-Color&#8221; direction=&#8221;horizontal&#8221; columns=&#8221;2&#8243; icon_size=&#8221;small&#8221; icon_style=&#8221;border&#8221;][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055357374-5&#8243; tab_id=&#8221;1763055357374-3&#8243; header=&#8221;Language generation&#8221; text=&#8221;Formulate the answer in natural language.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055357385-3&#8243; tab_id=&#8221;1763055357385-6&#8243; header=&#8221;Style adaptation&#8221; text=&#8221;Choice of tone, tempo and register according to the context.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055357399-5&#8243; tab_id=&#8221;1763055357400-6&#8243; header=&#8221;Structuring&#8221; text=&#8221;Organization of content in paragraphs, lists and semantic transitions.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055357413-5&#8243; tab_id=&#8221;1763055357414-10&#8243; header=&#8221;Length optimization&#8221; text=&#8221;Comparison of information density and readability.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055357428-2&#8243; tab_id=&#8221;1763055357429-10&#8243; header=&#8221;Precision check&#8221; text=&#8221;Check for clarity, consistency and semantic accuracy.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055357443-5&#8243; tab_id=&#8221;1763055357444-10&#8243; header=&#8221;Final Response Output&#8221; text=&#8221;Output of the finished answer &#8211; the visible surface of the entire thought process.&#8221;][\/nectar_icon_list_item][\/nectar_icon_list][divider line_type=&#8221;Full Width Line&#8221; line_thickness=&#8221;4&#8243; divider_color=&#8221;default&#8221; custom_height=&#8221;30&#8243;][vc_row_inner column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; bottom_padding=&#8221;2%&#8221; text_align=&#8221;left&#8221; row_position=&#8221;default&#8221; row_position_tablet=&#8221;inherit&#8221; row_position_phone=&#8221;inherit&#8221; overflow=&#8221;visible&#8221; pointer_events=&#8221;all&#8221;][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; bottom_margin=&#8221;30&#8243; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;.vc_custom_1762715768164{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]In this final phase, the model leaves the realm of pure knowledge &#8211; it begins to speak. A <strong>linguistic structure<\/strong> that combines style, rhythm and precision emerges from the previously formed structure of meaning. The model transforms its inner logic into linear language so that thoughts and weightings become readable for people.<\/p>\n<p>First of all, the system decides <strong>how<\/strong> it responds: sober, explanatory or argumentative. For factual topics, clarity dominates &#8211; for analytical or strategic topics, a rhythmic, context-rich style. The model simulates communication competence: it selects sentence lengths, word fields and rhetorical transitions in such a way that they correspond to the user&#8217;s presumed expectations.<\/p>\n<p><strong>The answer<\/strong> is then <strong>structured<\/strong>: paragraphs, lists and semantic markers organize the previously condensed arguments. Each formulation is a controlled recombination of meaning and probability &#8211; not free creativity, but precise implementation of the previously calculated semantic framework.<\/p>\n<p>In parallel, a constant <strong>self-monitoring<\/strong> process takes place: the model checks whether each formulation carries the intended content, removes redundancies, corrects implicit contradictions and smoothes out stylistic breaks. The result is a text that is coherent, readable and self-contained &#8211; the linguistic manifestation of a probabilistic truth.<\/p>\n<p>For brands, this phase is the <strong>projection screen<\/strong> for their digital identity. This is where it becomes clear how the machine &#8220;tells&#8221; them: factual, trustworthy, concise &#8211; or fragmented and arbitrary. Those who have previously created semantic clarity are <strong>precisely reconstructed<\/strong> here. Those who communicate in an unstructured manner are <strong>rendered in a blurred<\/strong> manner in this phase.[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row type=&#8221;full_width_content&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;60px&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; top_padding=&#8221;2%&#8221; constrain_group_1=&#8221;yes&#8221; bottom_padding=&#8221;2%&#8221; top_margin=&#8221;4%&#8221; constrain_group_7=&#8221;yes&#8221; bottom_margin=&#8221;4%&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overflow=&#8221;visible&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; enable_shape_divider=&#8221;true&#8221; shape_divider_color=&#8221;#000000&#8243; shape_divider_position=&#8221;both&#8221; shape_divider_height=&#8221;5&#8243; bg_image_animation=&#8221;none&#8221; gradient_type=&#8221;default&#8221; shape_type=&#8221;straight_section&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; top_margin=&#8221;3%&#8221; bottom_margin=&#8221;3%&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/2&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][nectar_video_player_self_hosted video_mp4=&#8221;https:\/\/norbert-kathriner.ch\/wp-content\/uploads\/2025\/07\/Mach-deine-Marke-sichtbar-fuer-KI.mp4&#8243; el_aspect=&#8221;11&#8243; el_aspect_tablet=&#8221;11&#8243; el_aspect_phone=&#8221;11&#8243; align=&#8221;center&#8221; player_functionality=&#8221;default&#8221; hide_controls=&#8221;yes&#8221; loop=&#8221;yes&#8221; autoplay=&#8221;yes&#8221; video_loading=&#8221;default&#8221; border_radius=&#8221;none&#8221; box_shadow=&#8221;none&#8221; advanced_gradient_display_type=&#8221;linear&#8221;][\/vc_column][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/2&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][vc_column_text css=&#8221;.vc_custom_1755287754380{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]<strong>AI Visibility offer<\/strong><br \/>\nVisible to people. Visible for machines.<\/p>\n<p>When AI decides what is visible, no campaign or corporate design will help. Only structure.<\/p>\n<p>\u2192 <a href=\"https:\/\/norbert-kathriner.ch\/en\/ai-visibility-offer-have-your-visibility-checked-now\/\">To the offer <\/a><\/p>\n<p>\u2192 <a href=\"https:\/\/www.perplexity.ai\/search\/nenne-mir-agenturen-mit-echter-O7jrHox3R7KXw9D8hEvBkA?0=d\" target=\"_blank\" rel=\"noopener\">To the offer analysis<\/a>[\/vc_column_text][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; top_padding=&#8221;2%&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overflow=&#8221;visible&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_color=&#8221;#000000&#8243; shape_divider_position=&#8221;top&#8221; shape_divider_height=&#8221;5&#8243; bg_image_animation=&#8221;none&#8221; gradient_type=&#8221;default&#8221; shape_type=&#8221;straight_section&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; top_margin=&#8221;30&#8243; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;.vc_custom_1762715055612{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h2><span class=\"ez-toc-section\" id=\"Part_II_%E2%80%93_Case_LO_answer_with_online_research\"><\/span>Part II &#8211; Case L+O (answer with online research)<span class=\"ez-toc-section-end\"><\/span><\/h2>\n[\/vc_column_text][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; top_padding=&#8221;2%&#8221; bottom_padding=&#8221;2%&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overflow=&#8221;visible&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_color=&#8221;#000000&#8243; shape_divider_position=&#8221;top&#8221; shape_divider_height=&#8221;5&#8243; bg_image_animation=&#8221;none&#8221; gradient_type=&#8221;default&#8221; shape_type=&#8221;straight_section&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][nectar_badge display_tag=&#8221;body&#8221; badge_style=&#8221;default&#8221; bg_color_type=&#8221;global&#8221; color=&#8221;extra-color-gradient-2&#8243; text_color=&#8221;#ffffff&#8221; padding=&#8221;medium&#8221; border_radius=&#8221;3px&#8221; display=&#8221;block&#8221; text=&#8221;Phase 0&#8243;][vc_column_text css=&#8221;.vc_custom_1762715101973{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h3>Prompt Input \/ Intent Parsing<\/h3>\n[\/vc_column_text][divider line_type=&#8221;Full Width Line&#8221; line_thickness=&#8221;4&#8243; divider_color=&#8221;default&#8221; custom_height=&#8221;30&#8243;][vc_column_text css=&#8221;.vc_custom_1762623036738{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]The LLM process steps[\/vc_column_text][nectar_icon_list color=&#8221;Accent-Color&#8221; direction=&#8221;horizontal&#8221; columns=&#8221;2&#8243; icon_size=&#8221;small&#8221; icon_style=&#8221;border&#8221;][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055357637-3&#8243; tab_id=&#8221;1763055357637-1&#8243; header=&#8221;Prompt reception&#8221; text=&#8221;Processing the user input.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055357647-1&#8243; tab_id=&#8221;1763055357648-5&#8243; header=&#8221;Tokenization&#8221; text=&#8221;Breakdown into processable units.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055357654-1&#8243; tab_id=&#8221;1763055357655-10&#8243; header=&#8221;Semantic analysis&#8221; text=&#8221;Recognition of intent, topic, entities and relations.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055357670-0&#8243; tab_id=&#8221;1763055357670-4&#8243; header=&#8221;Intent vector creation&#8221; text=&#8221;Abstract representation of the request.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055357676-2&#8243; tab_id=&#8221;1763055357677-4&#8243; header=&#8221;Complexity assessment&#8221; text=&#8221;Assessment of the response type and document requirements.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055357692-1&#8243; tab_id=&#8221;1763055357692-1&#8243; header=&#8221;Gap detection&#8221; text=&#8221;Comparison between internal knowledge and potential information requirements.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055357708-10&#8243; tab_id=&#8221;1763055357709-5&#8243; header=&#8221;Decision preparation&#8221; text=&#8221;Preparation of the switch &#8211; remain internal or open search area.&#8221;][\/nectar_icon_list_item][\/nectar_icon_list][divider line_type=&#8221;Full Width Line&#8221; line_thickness=&#8221;4&#8243; divider_color=&#8221;default&#8221; custom_height=&#8221;30&#8243;][vc_row_inner column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; bottom_padding=&#8221;2%&#8221; text_align=&#8221;left&#8221; row_position=&#8221;default&#8221; row_position_tablet=&#8221;inherit&#8221; row_position_phone=&#8221;inherit&#8221; overflow=&#8221;visible&#8221; pointer_events=&#8221;all&#8221;][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; bottom_margin=&#8221;30&#8243; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;.vc_custom_1762716125460{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]In the hybrid path, too, everything begins with understanding &#8211; but the goal is different. While the internal mode (Case L) checks <em>what<\/em> is already known, the hybrid path aims to <strong>make gaps in knowledge visible<\/strong>.<br \/>\nThe model interprets the query not only semantically, but also epistemically: <em>Do I know enough to be able to answer?<\/em><\/p>\n<p>To do this, it breaks down the user input into tokens, recognizes semantic clusters, entities and relations and uses them to form an <strong>intent vector<\/strong>. This intent vector describes the target content space and serves as a control signal for all subsequent layers. At the same time, the <strong>complexity<\/strong> of the query is evaluated &#8211; is it a definitional, factual or argumentative question? The result is a <strong>confidence profile<\/strong> that already reveals initial uncertainties and thus prepares the decision as to whether the search space needs to be opened.<\/p>\n<p>This phase is crucial for brands: this is where it is decided whether a brand is <strong>even considered as a potential source<\/strong>. Only clearly structured, semantically precise communication increases the likelihood that the model will mark it as a relevant candidate for evidence in the next step.[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; top_padding=&#8221;2%&#8221; bottom_padding=&#8221;2%&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overflow=&#8221;visible&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_color=&#8221;#000000&#8243; shape_divider_position=&#8221;top&#8221; shape_divider_height=&#8221;5&#8243; bg_image_animation=&#8221;none&#8221; gradient_type=&#8221;default&#8221; shape_type=&#8221;straight_section&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][nectar_badge display_tag=&#8221;body&#8221; badge_style=&#8221;default&#8221; bg_color_type=&#8221;global&#8221; color=&#8221;extra-color-gradient-2&#8243; text_color=&#8221;#ffffff&#8221; padding=&#8221;medium&#8221; border_radius=&#8221;3px&#8221; display=&#8221;block&#8221; text=&#8221;Phase 1&#8243;][vc_column_text css=&#8221;.vc_custom_1762716295023{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h3>Internal Knowledge Retrieval &amp; Gap Detection<\/h3>\n[\/vc_column_text][divider line_type=&#8221;Full Width Line&#8221; line_thickness=&#8221;4&#8243; divider_color=&#8221;default&#8221; custom_height=&#8221;30&#8243;][vc_column_text css=&#8221;.vc_custom_1762623036738{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]The LLM process steps[\/vc_column_text][nectar_icon_list color=&#8221;Accent-Color&#8221; direction=&#8221;horizontal&#8221; columns=&#8221;2&#8243; icon_size=&#8221;small&#8221; icon_style=&#8221;border&#8221;][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055357924-0&#8243; tab_id=&#8221;1763055357925-9&#8243; header=&#8221;Parametric knowledge retrieval&#8221; text=&#8221;Access to trained weights.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055357936-7&#8243; tab_id=&#8221;1763055357937-2&#8243; header=&#8221;Concept activation&#8221; text=&#8221;Relevant knowledge patterns are activated.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055357952-8&#8243; tab_id=&#8221;1763055357952-5&#8243; header=&#8221;Sample extraction&#8221; text=&#8221;Recall of memorized structures and contexts.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055357967-1&#8243; tab_id=&#8221;1763055357967-4&#8243; header=&#8221;Contextual filtering&#8221; text=&#8221;Limitation to relevant areas of knowledge.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055357983-5&#8243; tab_id=&#8221;1763055357984-10&#8243; header=&#8221;Confidence score calculation&#8221; text=&#8221;Assessment of the internal strength of evidence.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055358001-4&#8243; tab_id=&#8221;1763055358001-4&#8243; header=&#8221;Actuality check&#8221; text=&#8221;Comparison of the topic area with the knowledge cut-off.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055358018-10&#8243; tab_id=&#8221;1763055358019-1&#8243; header=&#8221;Gap identification&#8221; text=&#8221;Marking of missing or outdated information.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055358036-2&#8243; tab_id=&#8221;1763055358036-3&#8243; header=&#8221;Sufficiency check&#8221; text=&#8221;Decision as to whether internal evidence is sufficient \u2192 no \u2192 Activation of the search phase.&#8221;][\/nectar_icon_list_item][\/nectar_icon_list][divider line_type=&#8221;Full Width Line&#8221; line_thickness=&#8221;4&#8243; divider_color=&#8221;default&#8221; custom_height=&#8221;30&#8243;][vc_row_inner column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; bottom_padding=&#8221;2%&#8221; text_align=&#8221;left&#8221; row_position=&#8221;default&#8221; row_position_tablet=&#8221;inherit&#8221; row_position_phone=&#8221;inherit&#8221; overflow=&#8221;visible&#8221; pointer_events=&#8221;all&#8221;][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; bottom_margin=&#8221;30&#8243; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;.vc_custom_1762716368764{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]In this phase, the model performs a double movement: It first draws on its internal knowledge &#8211; as if it were &#8220;remembering&#8221; &#8211; and at the same time checks <strong>whether this remembering is sufficient<\/strong>. Phase 1 thus forms the bridge between the model&#8217;s familiar mental space and the as yet unknown external information space.<\/p>\n<p>Technically, the model activates its trained weights, retrieves stored concepts and patterns and evaluates their relevance to the current query. It creates an <strong>internal image of the question<\/strong>, but this image is probabilistic &#8211; not a reference book. The model therefore does not know <em>whether<\/em> it knows the answer, but only <em>how certain<\/em> it is that the existing patterns are sufficient.<\/p>\n<p>The <strong>up-to-dateness check<\/strong> is central: the recognized topic area is compared with the user&#8217;s own knowledge period. If the query concerns events after the training cut-off or contains terms that are not yet established in the training corpus, the system marks these areas as <em>uncertain<\/em> or <em>potentially outdated<\/em>. Semantic gaps &#8211; e.g. new institutions, technologies or terms &#8211; are also recorded in this way.<\/p>\n<p>These individual assessments are used to create a <strong>confidence profile<\/strong>, a matrix of internal certainty and suspected knowledge gaps. If the internal evidence is deemed insufficient, the transition to an external search (fan-out queries) is activated.<\/p>\n<p>For brands, this means that this is where machine memory ends &#8211; and this is where the <strong>opportunity to become visible through up-to-date, structured data<\/strong> begins. Anyone who regularly publishes structured information (e.g. via JSON-LD, Q-IDs or Schema.org) becomes a natural anchor in this phase. Machines then do not fall back on arbitrary sources, but on those that they recognize as <strong>reliable and semantically coherent<\/strong>.[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; top_padding=&#8221;2%&#8221; bottom_padding=&#8221;2%&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overflow=&#8221;visible&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_color=&#8221;#000000&#8243; shape_divider_position=&#8221;top&#8221; shape_divider_height=&#8221;5&#8243; bg_image_animation=&#8221;none&#8221; gradient_type=&#8221;default&#8221; shape_type=&#8221;straight_section&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][nectar_badge display_tag=&#8221;body&#8221; badge_style=&#8221;default&#8221; bg_color_type=&#8221;global&#8221; color=&#8221;extra-color-gradient-2&#8243; text_color=&#8221;#ffffff&#8221; padding=&#8221;medium&#8221; border_radius=&#8221;3px&#8221; display=&#8221;block&#8221; text=&#8221;Phase 2&#8243;][vc_column_text css=&#8221;.vc_custom_1762717351682{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h3>Fan-Out Query Generation<\/h3>\n[\/vc_column_text][divider line_type=&#8221;Full Width Line&#8221; line_thickness=&#8221;4&#8243; divider_color=&#8221;default&#8221; custom_height=&#8221;30&#8243;][vc_column_text css=&#8221;.vc_custom_1762623036738{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]The LLM process steps[\/vc_column_text][nectar_icon_list color=&#8221;Accent-Color&#8221; direction=&#8221;horizontal&#8221; columns=&#8221;2&#8243; icon_size=&#8221;small&#8221; icon_style=&#8221;border&#8221;][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055358222-9&#8243; tab_id=&#8221;1763055358223-2&#8243; header=&#8221;Research needs analysis&#8221; text=&#8221;Determination of the missing information components.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055358233-0&#8243; tab_id=&#8221;1763055358233-7&#8243; header=&#8221;Query generation&#8221; text=&#8221;Creation of several semantically varied search queries.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055358240-8&#8243; tab_id=&#8221;1763055358240-8&#8243; header=&#8221;Semantic question vector&#8221; text=&#8221;Extraction of topics, entities and relations.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055358247-7&#8243; tab_id=&#8221;1763055358247-2&#8243; header=&#8221;Query optimization&#8221; text=&#8221;Reduction to concise, search engine compatible terms (1-6 tokens).&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055358264-6&#8243; tab_id=&#8221;1763055358265-7&#8243; header=&#8221;Paraphrase Diversification&#8221; text=&#8221;Linguistic variation to increase coverage.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055358281-3&#8243; tab_id=&#8221;1763055358282-9&#8243; header=&#8221;Exploration-exploitation balance&#8221; text=&#8221;Combination of new and established search paths.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055358299-5&#8243; tab_id=&#8221;1763055358299-1&#8243; header=&#8221;Prioritization&#8221; text=&#8221;Ranking of queries according to relevance, precision and likelihood of success.&#8221;][\/nectar_icon_list_item][\/nectar_icon_list][divider line_type=&#8221;Full Width Line&#8221; line_thickness=&#8221;4&#8243; divider_color=&#8221;default&#8221; custom_height=&#8221;30&#8243;][vc_row_inner column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; bottom_padding=&#8221;2%&#8221; text_align=&#8221;left&#8221; row_position=&#8221;default&#8221; row_position_tablet=&#8221;inherit&#8221; row_position_phone=&#8221;inherit&#8221; overflow=&#8221;visible&#8221; pointer_events=&#8221;all&#8221;][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; bottom_margin=&#8221;30&#8243; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;.vc_custom_1762717924090{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]As soon as the model recognizes that its internal knowledge is not sufficient, it opens up the search space. Phase 2 marks the transition from passive memory to <strong>active research<\/strong> &#8211; the moment when thinking becomes action. The model begins to search specifically for evidence instead of relying solely on stored patterns.<\/p>\n<p>To do this, it translates the intent vector into a large number of semantically varied search queries &#8211; so-called <strong>fan-out queries<\/strong>. Each query is a hypothesis: a possible path to relevant evidence. Instead of formulating just one &#8220;perfect&#8221; search query, the model generates an entire <strong>search spectrum<\/strong> of synonyms, paraphrases, sub-terms and context combinations. The aim is to open up the <strong>candidate space<\/strong> as broadly as possible without losing semantic precision.<\/p>\n<p>An important aspect of this phase is the <strong>balance between exploration and exploitation<\/strong>: the model tries out new semantic paths (exploration), but also falls back on proven source patterns (exploitation). In this way, new insights are generated without losing stability. In addition, the model optimizes the queries syntactically &#8211; search queries are reduced to short, search engine-compatible phrases (typically 1-6 tokens) and often enriched with entities or identifiers. This increases the probability that subsequent retrieval systems (e.g. Google, Bing, internal APIs) will deliver high-quality hits.<\/p>\n<p>For brands, this phase means<\/p>\n<ul>\n<li><strong>Findability \u2260 Recommendation.<\/strong> <strong>Phase 2<\/strong> determines <strong>whether<\/strong> your content will even make it into the downstream review as a <strong>candidate<\/strong> &#8211; not whether it will be cited.<\/li>\n<li><strong>&#8220;Retrievability&#8221; is the lever now.<\/strong> Only content with clear <strong>semantic signatures<\/strong> (precise descriptions, consistent @id\/Q-IDs, clean sameAs links, lean search engine-compatible query terms) will reliably appear in the candidate space of search systems.<\/li>\n<li><strong>The actual decision is made in <em>phase 5 &#8211; evidence weighting<\/em>.<\/strong> This is where the <strong>evidence architecture<\/strong> (source density, consistency of relations, agreement across multiple sources) decides <strong>whether<\/strong> a retrievable document is selected by the machine as an <strong>authoritative source<\/strong> and <strong>recommended\/cited<\/strong>.<\/li>\n<\/ul>\n[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; top_padding=&#8221;2%&#8221; bottom_padding=&#8221;2%&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overflow=&#8221;visible&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_color=&#8221;#000000&#8243; shape_divider_position=&#8221;top&#8221; shape_divider_height=&#8221;5&#8243; bg_image_animation=&#8221;none&#8221; gradient_type=&#8221;default&#8221; shape_type=&#8221;straight_section&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][nectar_badge display_tag=&#8221;body&#8221; badge_style=&#8221;default&#8221; bg_color_type=&#8221;global&#8221; color=&#8221;extra-color-gradient-2&#8243; text_color=&#8221;#ffffff&#8221; padding=&#8221;medium&#8221; border_radius=&#8221;3px&#8221; display=&#8221;block&#8221; text=&#8221;Phase 3&#8243;][vc_column_text css=&#8221;.vc_custom_1762717943708{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h3>Evidence Extraction<\/h3>\n[\/vc_column_text][divider line_type=&#8221;Full Width Line&#8221; line_thickness=&#8221;4&#8243; divider_color=&#8221;default&#8221; custom_height=&#8221;30&#8243;][vc_column_text css=&#8221;.vc_custom_1762623036738{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]The LLM process steps[\/vc_column_text][nectar_icon_list color=&#8221;Accent-Color&#8221; direction=&#8221;horizontal&#8221; columns=&#8221;2&#8243; icon_size=&#8221;small&#8221; icon_style=&#8221;border&#8221;][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055358528-1&#8243; tab_id=&#8221;1763055358528-4&#8243; header=&#8221;Web search execution&#8221; text=&#8221;Transmission of fan-out queries to external search interfaces.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055358538-8&#8243; tab_id=&#8221;1763055358539-6&#8243; header=&#8221;Results reception&#8221; text=&#8221;Retrieval of top URLs, snippets and metadata.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055358545-4&#8243; tab_id=&#8221;1763055358545-9&#8243; header=&#8221;Preliminary examination&#8221; text=&#8221;Evaluation of the hits according to relevance, topicality and structural readability.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055358562-10&#8243; tab_id=&#8221;1763055358562-7&#8243; header=&#8221;HTML parsing&#8221; text=&#8221;Analysis of the page structure, extraction of relevant DOM areas.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055358570-4&#8243; tab_id=&#8221;1763055358570-1&#8243; header=&#8221;JSON-LD, RDFa and microdata recognition&#8221; text=&#8221;Identification of existing structured data.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055358577-3&#8243; tab_id=&#8221;1763055358577-1&#8243; header=&#8221;Passage ranking&#8221; text=&#8221;Prioritization of content-relevant text sections based on the intent vector.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055358597-6&#8243; tab_id=&#8221;1763055358597-4&#8243; header=&#8221;Evidence list creation&#8221; text=&#8221;Compilation of all potential evidence for the subsequent entity analysis.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055358616-9&#8243; tab_id=&#8221;1763055358617-0&#8243; header=&#8221;Feedback loop&#8221; text=&#8221;If the relevance is insufficient, return to phase 2 (Fan-Out Query Refinement).&#8221;][\/nectar_icon_list_item][\/nectar_icon_list][divider line_type=&#8221;Full Width Line&#8221; line_thickness=&#8221;4&#8243; divider_color=&#8221;default&#8221; custom_height=&#8221;30&#8243;][vc_row_inner column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; bottom_padding=&#8221;2%&#8221; text_align=&#8221;left&#8221; row_position=&#8221;default&#8221; row_position_tablet=&#8221;inherit&#8221; row_position_phone=&#8221;inherit&#8221; overflow=&#8221;visible&#8221; pointer_events=&#8221;all&#8221;][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; bottom_margin=&#8221;30&#8243; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;.vc_custom_1762718011833{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]Once the fan-out queries have been generated and prioritized, the model begins the actual search. It now enters the operational search space and retrieves the generated search queries via external interfaces &#8211; such as search engines, specialized databases or API-based knowledge sources. The result is a large number of hits: URLs, snippets and metadata. Phase 3 thus forms the <strong>link between search and understanding<\/strong> &#8211; this is where hits are turned into structured, usable material for the first time.<\/p>\n<p>The process is similar to an automated literature search: each source is checked for semantic relevance, topicality and structural connectivity. Documents that already contain <strong>structured data formats<\/strong> &#8211; such as JSON-LD, RDFa or microdata &#8211; are particularly valuable. These formats provide explicit references to entities, relationships and document structures and thus significantly increase the <strong>evidential value<\/strong> of the source.<\/p>\n<p>The model extracts <strong>passages and structural elements<\/strong> from the HTML and metadata context that correspond to the original intent vector. It prioritizes those text passages in which semantic agreement, entity density and document potential coincide. This results in an <strong>evidence set<\/strong> consisting of individual text fragments, tables, meta fields and structured statements.<\/p>\n<p>The transition to phase 4 is initiated as soon as enough raw material has been collected to start the <strong>entity and relationship analysis<\/strong>. If relevance or structure is missing, the search space is reopened via feedback loops (return to phase 2).<\/p>\n<p>For brands, this is the moment when machine visibility becomes <strong>measurable<\/strong> &#8211; but not yet <strong>evaluated<\/strong>. A document can perform well in this phase, but fall out again in phase 5 (Evidence Weighting) if its structure or document architecture is weak. Fan-out provides visibility in the candidate space, <strong>evidence extraction provides context<\/strong>, and only <strong>evidence weighting determines authority.<\/strong>[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; top_padding=&#8221;2%&#8221; bottom_padding=&#8221;2%&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overflow=&#8221;visible&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_color=&#8221;#000000&#8243; shape_divider_position=&#8221;top&#8221; shape_divider_height=&#8221;5&#8243; bg_image_animation=&#8221;none&#8221; gradient_type=&#8221;default&#8221; shape_type=&#8221;straight_section&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][nectar_badge display_tag=&#8221;body&#8221; badge_style=&#8221;default&#8221; bg_color_type=&#8221;global&#8221; color=&#8221;extra-color-gradient-2&#8243; text_color=&#8221;#ffffff&#8221; padding=&#8221;medium&#8221; border_radius=&#8221;3px&#8221; display=&#8221;block&#8221; text=&#8221;Phase 4&#8243;][vc_column_text css=&#8221;.vc_custom_1762718313542{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h3>Entity Linking (@id \/ sameAs \/ Q-IDs verification)<\/h3>\n[\/vc_column_text][divider line_type=&#8221;Full Width Line&#8221; line_thickness=&#8221;4&#8243; divider_color=&#8221;default&#8221; custom_height=&#8221;30&#8243;][vc_column_text css=&#8221;.vc_custom_1762623036738{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]The LLM process steps[\/vc_column_text][nectar_icon_list color=&#8221;Accent-Color&#8221; direction=&#8221;horizontal&#8221; columns=&#8221;2&#8243; icon_size=&#8221;small&#8221; icon_style=&#8221;border&#8221;][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055358913-1&#8243; tab_id=&#8221;1763055358914-1&#8243; header=&#8221;Entity recognition&#8221; text=&#8221;Identification of relevant concepts and actors in the text and in structured data.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055358924-6&#8243; tab_id=&#8221;1763055358924-10&#8243; header=&#8221;Schema type identification&#8221; text=&#8221;Assignment to known Schema.org types (e.g. Organization, Person, CreativeWork).&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055358930-9&#8243; tab_id=&#8221;1763055358931-5&#8243; header=&#8221;@id extraction&#8221; text=&#8221;Reading unique identifiers within the structured data.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055358948-0&#8243; tab_id=&#8221;1763055358948-2&#8243; header=&#8221;sameAs link&#8221; text=&#8221;Tracking of external references (e.g. Wikidata, Wikipedia, Crunchbase).&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055358966-6&#8243; tab_id=&#8221;1763055358967-7&#8243; header=&#8221;Identifier parsing&#8221; text=&#8221;Processing of standardized IDs such as Q-ID, ISIN, LEI, ORCID etc.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055358975-8&#8243; tab_id=&#8221;1763055358975-3&#8243; header=&#8221;Trained entity matching&#8221; text=&#8221;Comparison of external entities with internally learned patterns.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055358993-2&#8243; tab_id=&#8221;1763055358994-0&#8243; header=&#8221;Cross-Document Entity Coreference&#8221; text=&#8221;Recognition of identical entities across multiple sources.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055359012-7&#8243; tab_id=&#8221;1763055359012-3&#8243; header=&#8221;Verification via structured data&#8221; text=&#8221;Confirmation by existing machine-readable anchors.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055359021-8&#8243; tab_id=&#8221;1763055359021-8&#8243; header=&#8221;Graph construction&#8221; text=&#8221;Construction of a temporary entity and relationship graph.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055359040-8&#8243; tab_id=&#8221;1763055359041-2&#8243; header=&#8221;Transitive reasoning&#8221; text=&#8221;Conclusion about indirect connections between entities (A \u2192 B \u2192 C).&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055359050-7&#8243; tab_id=&#8221;1763055359051-1&#8243; header=&#8221;Consolidation&#8221; text=&#8221;Merging of identical or redundant entities.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055359058-4&#8243; tab_id=&#8221;1763055359059-2&#8243; header=&#8221;Contextualization&#8221; text=&#8221;Embedding in the overall thematic context of the research.&#8221;][\/nectar_icon_list_item][\/nectar_icon_list][divider line_type=&#8221;Full Width Line&#8221; line_thickness=&#8221;4&#8243; divider_color=&#8221;default&#8221; custom_height=&#8221;30&#8243;][vc_row_inner column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; bottom_padding=&#8221;2%&#8221; text_align=&#8221;left&#8221; row_position=&#8221;default&#8221; row_position_tablet=&#8221;inherit&#8221; row_position_phone=&#8221;inherit&#8221; overflow=&#8221;visible&#8221; pointer_events=&#8221;all&#8221;][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; bottom_margin=&#8221;30&#8243; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;.vc_custom_1762719314930{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]Once the relevant content has been extracted, the actual semantic work begins: the model attempts to understand <strong>who or what<\/strong> actually appears in the texts found. Here, terms, names and concepts are converted into machine-readable entities &#8211; and linked with each other.<\/p>\n<p>The model identifies entities on two levels:<\/p>\n<ol>\n<li><strong>Textual level:<\/strong> people, organizations, places, products, time periods or events in continuous text.<\/li>\n<li><strong>Structural level:<\/strong> Entities that are already defined via machine-readable anchors &#8211; such as @id, sameAs, identifier, Q-IDs or Schema.org types.<\/li>\n<\/ol>\n<p>The two layers are <strong>compared and merged<\/strong>. For example, if &#8220;Allianz&#8221; appears in the continuous text and an entity with Q487292 (Wikidata ID of Allianz SE) is present in the JSON-LD layer at the same time, the model recognizes a unique assignment. If this anchor is missing, probabilistic matching is performed on the basis of semantic proximity and context match.<\/p>\n<p>The next step is to create a <strong>temporary knowledge graph<\/strong>: Nodes represent entities, edges their relationships &#8211; e.g. &#8220;Allianz SE \u2194 operates \u2194 investor relations portal \u2194 publishes \u2194 quarterly report&#8221;. The model analyzes the graphical structure for consistency, redundancy and transitivity: If A is linked to B and B to C, it is often possible to infer A \u2192 C &#8211; this <strong>transitive reasoning via entity paths<\/strong> is crucial for verifying evidence across document boundaries.<\/p>\n<p>This phase is of high strategic relevance for brands: This is where it is decided whether they appear in the machine&#8217;s knowledge graph <strong>as a clearly identifiable entity<\/strong> or are lost as a generic term. Only entities with stable anchors (@id, sameAs, Q-IDs, LEIs, ISINs, etc.) are systematically recognized and evaluated as <strong>trustworthy references<\/strong>. Machines do not see logos or slogans &#8211; they recognize <strong>relationship networks<\/strong>.[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; top_padding=&#8221;2%&#8221; bottom_padding=&#8221;2%&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overflow=&#8221;visible&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_color=&#8221;#000000&#8243; shape_divider_position=&#8221;top&#8221; shape_divider_height=&#8221;5&#8243; bg_image_animation=&#8221;none&#8221; gradient_type=&#8221;default&#8221; shape_type=&#8221;straight_section&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][nectar_badge display_tag=&#8221;body&#8221; badge_style=&#8221;default&#8221; bg_color_type=&#8221;global&#8221; color=&#8221;extra-color-gradient-2&#8243; text_color=&#8221;#ffffff&#8221; padding=&#8221;medium&#8221; border_radius=&#8221;3px&#8221; display=&#8221;block&#8221; text=&#8221;Phase 5&#8243;][vc_column_text css=&#8221;.vc_custom_1762718709795{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h3>Evidence weighting (document architecture + diversity of sources)<\/h3>\n[\/vc_column_text][divider line_type=&#8221;Full Width Line&#8221; line_thickness=&#8221;4&#8243; divider_color=&#8221;default&#8221; custom_height=&#8221;30&#8243;][vc_column_text css=&#8221;.vc_custom_1762623036738{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]The LLM process steps[\/vc_column_text][nectar_icon_list color=&#8221;Accent-Color&#8221; direction=&#8221;horizontal&#8221; columns=&#8221;2&#8243; icon_size=&#8221;small&#8221; icon_style=&#8221;border&#8221;][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055359198-5&#8243; tab_id=&#8221;1763055359198-3&#8243; header=&#8221;Document architecture analysis&#8221; text=&#8221;Assessment of the structural quality of the data.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055359208-0&#8243; tab_id=&#8221;1763055359208-4&#8243; header=&#8221;Source diversity check&#8221; text=&#8221;Analysis of the diversity and mutual confirmation of sources.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055359214-6&#8243; tab_id=&#8221;1763055359214-5&#8243; header=&#8221;Frequency analysis&#8221; text=&#8221;Determination of the frequency of certain statements across different documents.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055359231-9&#8243; tab_id=&#8221;1763055359231-0&#8243; header=&#8221;Coherence check&#8221; text=&#8221;Comparison for consistency between sources.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055359252-6&#8243; tab_id=&#8221;1763055359252-2&#8243; header=&#8221;Authority weighting&#8221; text=&#8221;Evaluation of domain and source reputation.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055359269-8&#8243; tab_id=&#8221;1763055359270-10&#8243; header=&#8221;Topicality scoring&#8221; text=&#8221;Evaluation of the temporal relevance of the information.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055359287-4&#8243; tab_id=&#8221;1763055359288-9&#8243; header=&#8221;Calculation of evidential value&#8221; text=&#8221;Integration of anchors (Q-IDs, sameAs, identifiers) into the evaluation logic.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055359306-0&#8243; tab_id=&#8221;1763055359306-1&#8243; header=&#8221;Evidence Graph Construction&#8221; text=&#8221;Construction of a directed semantic proof graph.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055359323-10&#8243; tab_id=&#8221;1763055359323-9&#8243; header=&#8221;Evidence matrix creation&#8221; text=&#8221;Merging of all scores into an overall assessment of document strength.&#8221;][\/nectar_icon_list_item][\/nectar_icon_list][divider line_type=&#8221;Full Width Line&#8221; line_thickness=&#8221;4&#8243; divider_color=&#8221;default&#8221; custom_height=&#8221;30&#8243;][vc_row_inner column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; bottom_padding=&#8221;2%&#8221; text_align=&#8221;left&#8221; row_position=&#8221;default&#8221; row_position_tablet=&#8221;inherit&#8221; row_position_phone=&#8221;inherit&#8221; overflow=&#8221;visible&#8221; pointer_events=&#8221;all&#8221;][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; bottom_margin=&#8221;30&#8243; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;.vc_custom_1762718778232{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]Once the entities have been recognized and linked, the central question is: <strong>Which of this information is really reliable? <\/strong>In this phase, the model evaluates the <strong>strength of evidence of all the sources and data points collected<\/strong>. It sorts, weights and checks how consistent, how up-to-date and how structurally sound the evidence is.<\/p>\n<p>Each source is evaluated not only in terms of content, but also <strong>architecturally<\/strong>. Structural integrity &#8211; i.e. the existence of clear data relationships, recurring identifiers and referenced links &#8211; is interpreted as a sign of trustworthiness. The model constructs an <strong>evidence graph<\/strong> from these relationships, in which nodes represent entities and edges represent document relationships.<\/p>\n<p>This is followed by the actual weighting: sources with many stable anchors or facts that have been confirmed several times are given a high weighting. Contradictory or isolated statements are devalued. It is not the number of mentions that counts, but their <strong>coherence across different contexts<\/strong> &#8211; i.e. structural plausibility.<\/p>\n<p>The result of this phase is an <strong>evidence matrix<\/strong>: a multidimensional assessment of the quality, reliability and density of the collected data. This matrix is the basis for the subsequent reasoning: only what is considered reliable here may become part of the answer.<\/p>\n<p>For brands, this means that what could be described as <strong>machine trust<\/strong> is created. If you maintain your data consistently, update it regularly and link it across multiple platforms, you create the basis for being perceived as a <strong>stable truth<\/strong>. In this logic, visibility is no longer a volume phenomenon, but a <strong>product of trust<\/strong>.[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; top_padding=&#8221;2%&#8221; bottom_padding=&#8221;2%&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overflow=&#8221;visible&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_color=&#8221;#000000&#8243; shape_divider_position=&#8221;top&#8221; shape_divider_height=&#8221;5&#8243; bg_image_animation=&#8221;none&#8221; gradient_type=&#8221;default&#8221; shape_type=&#8221;straight_section&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][nectar_badge display_tag=&#8221;body&#8221; badge_style=&#8221;default&#8221; bg_color_type=&#8221;global&#8221; color=&#8221;extra-color-gradient-2&#8243; text_color=&#8221;#ffffff&#8221; padding=&#8221;medium&#8221; border_radius=&#8221;3px&#8221; display=&#8221;block&#8221; text=&#8221;Phase 5a&#8221;][vc_column_text css=&#8221;.vc_custom_1762719026681{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h3>Response Planning (Evidence-guided Outline)<\/h3>\n[\/vc_column_text][divider line_type=&#8221;Full Width Line&#8221; line_thickness=&#8221;4&#8243; divider_color=&#8221;default&#8221; custom_height=&#8221;30&#8243;][vc_column_text css=&#8221;.vc_custom_1762623036738{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]The LLM process steps[\/vc_column_text][nectar_icon_list color=&#8221;Accent-Color&#8221; direction=&#8221;horizontal&#8221; columns=&#8221;2&#8243; icon_size=&#8221;small&#8221; icon_style=&#8221;border&#8221;][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055359599-10&#8243; tab_id=&#8221;1763055359600-6&#8243; header=&#8221;Outline creation&#8221; text=&#8221;Creation of an evidence-based structure based on the evidence matrix.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055359610-5&#8243; tab_id=&#8221;1763055359610-2&#8243; header=&#8221;Claim prioritization&#8221; text=&#8221;Weighting of statements according to evidential value.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055359627-9&#8243; tab_id=&#8221;1763055359627-6&#8243; header=&#8221;Structuring via relations&#8221; text=&#8221;Use of semantic hierarchies (isPartOf, about, mentions) for argumentation order.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055359649-6&#8243; tab_id=&#8221;1763055359650-0&#8243; header=&#8221;Coherence check&#8221; text=&#8221;Checking the logical sequence and completeness of the outline.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055359677-7&#8243; tab_id=&#8221;1763055359677-3&#8243; header=&#8221;Feedback loop&#8221; text=&#8221;If the structure is unclear \u2192 return to phase 5 (evidence weighting) for readjustment.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055359699-0&#8243; tab_id=&#8221;1763055359699-0&#8243; header=&#8221;Transfer to Reasoning&#8221; text=&#8221;Forwarding of the organized evidence structure to phase 6 for synthesis.&#8221;][\/nectar_icon_list_item][\/nectar_icon_list][divider line_type=&#8221;Full Width Line&#8221; line_thickness=&#8221;4&#8243; divider_color=&#8221;default&#8221; custom_height=&#8221;30&#8243;][vc_row_inner column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; bottom_padding=&#8221;2%&#8221; text_align=&#8221;left&#8221; row_position=&#8221;default&#8221; row_position_tablet=&#8221;inherit&#8221; row_position_phone=&#8221;inherit&#8221; overflow=&#8221;visible&#8221; pointer_events=&#8221;all&#8221;][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; bottom_margin=&#8221;30&#8243; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;.vc_custom_1762719133149{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]Once the evidence matrix has been created and the evidential value of each source has been calculated, the model plans its response structure. This phase is the <strong>link between evaluation and justification<\/strong>: A linguistic-logical framework is created here from numbers and weights &#8211; an outline that determines what is mentioned first, what is mentioned next and what is not mentioned at all.<\/p>\n<p>The model creates an <strong>evidence-based argumentation structure<\/strong>. Each statement, each claim is prioritized according to its strength of evidence: High evidence values lead to central statements, weak evidence is integrated as marginal remarks or discarded altogether. Relationships from the evidence graph &#8211; such as isPartOf, about, mentions, hasPart &#8211; serve as hierarchical points of reference. This creates a <strong>semantic structure<\/strong> that determines the logical flow of the subsequent answer.<\/p>\n<p>The model also checks whether the line of reasoning is complete and free of contradictions. If evidence for an important section is missing, the system can access phase 5 again via a <strong>feedback loop<\/strong> and readjust the weighting or consider new sources. This mechanism ensures that the answer is not only correct, but also evidence-proportional &#8211; i.e. in line with the evidence.<\/p>\n<p>For brands, this phase means that it is decided here whether they form the central theme of the machine&#8217;s argumentation or only appear in passing. Those who provide consistent, logically linked data are perceived by the model as a structuring element of the answer &#8211; not as a footnote.[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; top_padding=&#8221;2%&#8221; bottom_padding=&#8221;2%&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overflow=&#8221;visible&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_color=&#8221;#000000&#8243; shape_divider_position=&#8221;top&#8221; shape_divider_height=&#8221;5&#8243; bg_image_animation=&#8221;none&#8221; gradient_type=&#8221;default&#8221; shape_type=&#8221;straight_section&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][nectar_badge display_tag=&#8221;body&#8221; badge_style=&#8221;default&#8221; bg_color_type=&#8221;global&#8221; color=&#8221;extra-color-gradient-2&#8243; text_color=&#8221;#ffffff&#8221; padding=&#8221;medium&#8221; border_radius=&#8221;3px&#8221; display=&#8221;block&#8221; text=&#8221;Phase 6&#8243;][vc_column_text css=&#8221;.vc_custom_1762719372726{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h3>Reasoning (Fusion L+O + Multi-hop)<\/h3>\n[\/vc_column_text][divider line_type=&#8221;Full Width Line&#8221; line_thickness=&#8221;4&#8243; divider_color=&#8221;default&#8221; custom_height=&#8221;30&#8243;][vc_column_text css=&#8221;.vc_custom_1762623036738{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]The LLM process steps[\/vc_column_text][nectar_icon_list color=&#8221;Accent-Color&#8221; direction=&#8221;horizontal&#8221; columns=&#8221;2&#8243; icon_size=&#8221;small&#8221; icon_style=&#8221;border&#8221;][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055360018-5&#8243; tab_id=&#8221;1763055360018-0&#8243; header=&#8221;Knowledge integration&#8221; text=&#8221;Merging internal (L) and external (O) evidence.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055360030-8&#8243; tab_id=&#8221;1763055360030-10&#8243; header=&#8221;Multi-hop reasoning&#8221; text=&#8221;Linking of documents across multiple entity and source paths.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055360051-10&#8243; tab_id=&#8221;1763055360051-1&#8243; header=&#8221;Chain-of-Thought (CoT)&#8221; text=&#8221;Breakdown of the conclusions into explicit intermediate steps.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055360070-2&#8243; tab_id=&#8221;1763055360070-4&#8243; header=&#8221;Process Reward Modeling (PRM)&#8221; text=&#8221;Evaluation of the logical quality of each reasoning step.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055360089-9&#8243; tab_id=&#8221;1763055360089-6&#8243; header=&#8221;Conflict Resolution&#8221; text=&#8221;Resolution of contradictory statements between internal and external sources.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055360108-6&#8243; tab_id=&#8221;1763055360109-5&#8243; header=&#8221;Self-verification&#8221; text=&#8221;Consistency check of interim results against evidence matrix and evidence graph.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055360130-8&#8243; tab_id=&#8221;1763055360130-0&#8243; header=&#8221;Contextual enrichment&#8221; text=&#8221;Supplementing missing connections with trained background knowledge.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055360150-1&#8243; tab_id=&#8221;1763055360151-9&#8243; header=&#8221;Recommendation Decision&#8221; text=&#8221;Classification of each source as a Mention or Recommendation (decisive for visibility).&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055360171-4&#8243; tab_id=&#8221;1763055360172-7&#8243; header=&#8221;Handover to phase 7&#8243; text=&#8221;Transition to the linguistic construction of the final answer.&#8221;][\/nectar_icon_list_item][\/nectar_icon_list][divider line_type=&#8221;Full Width Line&#8221; line_thickness=&#8221;4&#8243; divider_color=&#8221;default&#8221; custom_height=&#8221;30&#8243;][vc_row_inner column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; bottom_padding=&#8221;2%&#8221; text_align=&#8221;left&#8221; row_position=&#8221;default&#8221; row_position_tablet=&#8221;inherit&#8221; row_position_phone=&#8221;inherit&#8221; overflow=&#8221;visible&#8221; pointer_events=&#8221;all&#8221;][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; bottom_margin=&#8221;30&#8243; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;.vc_custom_1762719429755{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]In this phase, the model combines its two knowledge spaces &#8211; <strong>internal memory (L)<\/strong> and <strong>external evidence (O)<\/strong> &#8211; into a coherent rationale. This is where the actual <strong>layer of truth<\/strong> is created, in which knowledge and evidence are brought together, checked and weighted.<\/p>\n<p>The model checks which external evidence confirms, expands or contradicts its internal assumptions. To do this, it carries out a multi-stage <strong>, multi-hop reasoning<\/strong> process: Information is linked along the entity paths of the evidence graph. If source A contains an assertion about an entity and source B confirms the same entity via a secondary path, this link is strengthened &#8211; <strong>structured coherence<\/strong> is created.<\/p>\n<p>Reasoning does not follow a linear process, but rather a<strong>chain of<\/strong> intermediate steps<strong>(chain of thought<\/strong>). Each step is evaluated individually: a <strong>Process Reward Model (PRM)<\/strong> estimates the quality and logic of each intermediate step. In this way, the system recognizes faulty or redundant inference chains at an early stage and rejects them. At the same time, an internal <strong>self-verification<\/strong> runs, in which the model checks its own conclusions against the evidence matrix &#8211; a machine-based form of logical self-checking.<\/p>\n<p>The result is a consolidated, probabilistically based response structure in which every statement has a traceable evidence trail. The model now also decides <strong>whether<\/strong> a source is only evaluated as a <em>mention (Mention)<\/em> or as a <em>recommendation<\/em> &#8211; this is where relevance is separated from authority. A document that is frequently cited, structurally well linked and has been confirmed several times moves to the top of the priority list; all others remain in the background.<\/p>\n<p>For brands, this phase is the most critical in the entire process: this is where it is decided <strong>whether they are cited or recommended. <\/strong>It is not their visibility in retrieval (phase 2-3), but their semantic and structural quality in the evidence evaluation that determines whether the machine classifies them as a reliable source. AI visibility is therefore not a result of findability, but of <strong>evidence value and coherence in the reasoning process.<\/strong>[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; top_padding=&#8221;2%&#8221; bottom_padding=&#8221;2%&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overflow=&#8221;visible&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_color=&#8221;#000000&#8243; shape_divider_position=&#8221;top&#8221; shape_divider_height=&#8221;5&#8243; bg_image_animation=&#8221;none&#8221; gradient_type=&#8221;default&#8221; shape_type=&#8221;straight_section&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][nectar_badge display_tag=&#8221;body&#8221; badge_style=&#8221;default&#8221; bg_color_type=&#8221;global&#8221; color=&#8221;extra-color-gradient-2&#8243; text_color=&#8221;#ffffff&#8221; padding=&#8221;medium&#8221; border_radius=&#8221;3px&#8221; display=&#8221;block&#8221; text=&#8221;Phase 7&#8243;][vc_column_text css=&#8221;.vc_custom_1762719632954{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]\n<h3>Final Response Construction<\/h3>\n[\/vc_column_text][divider line_type=&#8221;Full Width Line&#8221; line_thickness=&#8221;4&#8243; divider_color=&#8221;default&#8221; custom_height=&#8221;30&#8243;][vc_column_text css=&#8221;.vc_custom_1762623036738{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]The LLM process steps[\/vc_column_text][nectar_icon_list color=&#8221;Accent-Color&#8221; direction=&#8221;horizontal&#8221; columns=&#8221;2&#8243; icon_size=&#8221;small&#8221; icon_style=&#8221;border&#8221;][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055360349-0&#8243; tab_id=&#8221;1763055360349-2&#8243; header=&#8221;Language generation&#8221; text=&#8221;Transformation of consolidated evidence into natural text.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055360360-5&#8243; tab_id=&#8221;1763055360360-5&#8243; header=&#8221;Argumentation structure&#8221; text=&#8221;Arrangement of the statements according to their strength of evidence and logical weighting.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055360378-4&#8243; tab_id=&#8221;1763055360379-0&#8243; header=&#8221;Citation integration&#8221; text=&#8221;Integration of structured metadata (url, name, publisher) for transparency.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055360396-4&#8243; tab_id=&#8221;1763055360397-9&#8243; header=&#8221;Style adaptation&#8221; text=&#8221;Choice of tone, register and linguistic rhythm according to the request.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055360416-9&#8243; tab_id=&#8221;1763055360416-8&#8243; header=&#8221;Structuring&#8221; text=&#8221;Division into paragraphs, semantic transitions and rhetorical hierarchies.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055360436-5&#8243; tab_id=&#8221;1763055360437-8&#8243; header=&#8221;Coherence and precision testing&#8221; text=&#8221;Fine control of logic, reading flow and accuracy.&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055360457-7&#8243; tab_id=&#8221;1763055360457-10&#8243; header=&#8221;Evidence transparency&#8221; text=&#8221;Labeling of the origin of knowledge (L, O or L+O).&#8221;][\/nectar_icon_list_item][nectar_icon_list_item icon_type=&#8221;numerical&#8221; text_full_html=&#8221;simple&#8221; title=&#8221;List Item&#8221; id=&#8221;1763055360475-9&#8243; tab_id=&#8221;1763055360476-7&#8243; header=&#8221;Final Response Output&#8221; text=&#8221;Output of the finished answer &#8211; the visible surface of a multi-stage reasoning process.&#8221;][\/nectar_icon_list_item][\/nectar_icon_list][divider line_type=&#8221;Full Width Line&#8221; line_thickness=&#8221;4&#8243; divider_color=&#8221;default&#8221; custom_height=&#8221;30&#8243;][vc_row_inner column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; bottom_padding=&#8221;2%&#8221; text_align=&#8221;left&#8221; row_position=&#8221;default&#8221; row_position_tablet=&#8221;inherit&#8221; row_position_phone=&#8221;inherit&#8221; overflow=&#8221;visible&#8221; pointer_events=&#8221;all&#8221;][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; bottom_margin=&#8221;30&#8243; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;.vc_custom_1762719708401{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]In this final phase, the previously synthesized evidence is translated into language. The model leaves the logical space of reasoning and enters the realm of <strong>communication<\/strong>. The probabilistic network of evidence, weightings and conclusions becomes a linear, readable text &#8211; the <strong>linguistic form of the machine truth layer<\/strong>.<\/p>\n<p>The model uses the outline created in phase 5a and the weighting calculated in phase 6 to <strong>construct<\/strong> the answer <strong>linguistically<\/strong>. It organizes the arguments according to the strength of their evidence, integrates references to sources and decides which evidence is explicitly mentioned or implicitly used. Language thus becomes a <strong>reasoning surface<\/strong> &#8211; each word is the result of a structured decision on evidence and relevance.<\/p>\n<p>At the same time, a multi-stage <strong>optimization of style and coherence<\/strong> takes place: the model selects tonality, rhythm and density according to the context of the query. A scientific question is answered precisely and formally, a strategic or brand-related question is given more narrative depth. A constant <strong>coherence check<\/strong> runs in the background: overlaps, logical leaps or stylistic breaks are corrected. The result is an answer that is not only correct, but also <strong>credible, fluid and context-sensitive<\/strong>.<\/p>\n<p>Another aspect is the <strong>transparency of origin<\/strong>: modern LLMs are increasingly integrating citation or source markers &#8211; such as url, name or publisher from Schema.org &#8211; to make it clear which data points were used for the justification. This gives the answer not only semantic, but also <strong>epistemic integrity<\/strong>.<\/p>\n<p>For brands, this phase means deciding <strong>how<\/strong> they are quoted, paraphrased or contextualized by machines. The type and clarity with which their structured data is linked determines <strong>whether they<\/strong> are <strong>cited as a source or ignored as background noise<\/strong>.[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overflow=&#8221;visible&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_position=&#8221;bottom&#8221; bg_image_animation=&#8221;none&#8221; gradient_type=&#8221;default&#8221; shape_type=&#8221;&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][vc_row_inner column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; text_align=&#8221;left&#8221; row_position=&#8221;default&#8221; row_position_tablet=&#8221;inherit&#8221; row_position_phone=&#8221;inherit&#8221; overflow=&#8221;visible&#8221; pointer_events=&#8221;all&#8221;][vc_column_inner column_padding=&#8221;padding-6-percent&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; top_margin=&#8221;30&#8243; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color=&#8221;#f5f5f7&#8243; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;5px&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;][vc_column_text css=&#8221;&#8221; text_direction=&#8221;default&#8221;]\n<h2><span class=\"ez-toc-section\" id=\"Conclusion_From_visibility_to_substance\"><\/span>Conclusion: From visibility to substance<span class=\"ez-toc-section-end\"><\/span><\/h2>\n[\/vc_column_text][vc_column_text css=&#8221;.vc_custom_1762727008101{margin-top: 35px !important;}&#8221; text_direction=&#8221;default&#8221;]The next level of digital communication is not a question of volume, but of readability in the system. If you want to understand why your brand appears &#8211; or does not appear &#8211; in ChatGPT, Perplexity or Bing, you have to look into the mechanisms of truth generation in language models.<\/p>\n<p>These models do not decide who is right, but what is provable. They prefer structures that are based on stable entities (Wikidata Q-IDs, Schema.org), consistent semantics (sameAs, @id) and verifiable links (isPartOf, about, mentions). The dual-path model shows that structured data works in both modes &#8211; transiently in search mode (Case L+O) and persistently through integration in future training cycles (Case L). Those who build up machine-readable evidence today secure both immediate visibility and long-term reputation.<\/p>\n<p>What the GEO industry sells as fan-out optimization falls short. Retrieval is only the beginning. The critical phase is evidence weighting (phase 5): This is where Mention separates from Recommendation. This is where it is decided whether a source is vaguely referenced or authoritatively cited. And this is precisely where Q-IDs, document architecture and cross-document entity coreference come into play &#8211; elements that no monitoring tool can measure because they operate in the reasoning layer, not the retrieval layer.<\/p>\n<p>AI visibility is therefore no longer a tactic &#8211; it is scientifically based brand management. Brands that communicate in a structured way become part of the knowledge space. Brands that do not do so remain anecdotes at the edge of a system that no longer reads stories but reconstructs contexts. And if you only measure signals, you remain an observer of a system that you cannot influence.[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; bottom_padding=&#8221;30&#8243; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overflow=&#8221;visible&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_position=&#8221;bottom&#8221; bg_image_animation=&#8221;none&#8221; gradient_type=&#8221;default&#8221; shape_type=&#8221;&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][vc_row_inner column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; text_align=&#8221;left&#8221; row_position=&#8221;default&#8221; row_position_tablet=&#8221;inherit&#8221; row_position_phone=&#8221;inherit&#8221; overflow=&#8221;visible&#8221; pointer_events=&#8221;all&#8221;][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; top_margin=&#8221;30&#8243; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;]<div class=\"nectar-fancy-ul\" data-list-icon=\"fa fa-link\" data-animation=\"false\" data-animation-delay=\"0\" data-color=\"accent-color\" data-spacing=\"default\" data-alignment=\"left\"> \n<h2><span class=\"ez-toc-section\" id=\"Link_tips\"><\/span>Link tips<span class=\"ez-toc-section-end\"><\/span><\/h2>\n <\/div>[divider line_type=&#8221;No Line&#8221; custom_height=&#8221;10&#8243;][vc_column_text css=&#8221;&#8221; text_direction=&#8221;default&#8221;]<strong>Scientifically based brand management through AI Visibility<\/strong><\/p>\n<p><a href=\"https:\/\/norbert-kathriner.ch\/en\/seo-brand-management-who-will-manage-brands-in-the-future-the-role-of-seo-ai\/\">SEO brand management: Who will manage brands in the future? The role of SEO &amp; AI <\/a><\/p>\n<p><a href=\"https:\/\/norbert-kathriner.ch\/en\/ai-supported-seo\/\">Brand management through AI-supported SEO &#8211; understand the change<\/a><\/p>\n<p><a href=\"https:\/\/norbert-kathriner.ch\/en\/machine-readable-or-irrelevant-why-companies-need-to-rethink-now\/\">Machine-readable or irrelevant &#8211; why companies need to rethink now<\/a><\/p>\n<p><a href=\"https:\/\/norbert-kathriner.ch\/en\/with-entities-to-machine-readable-structures-for-ai-visibility\/\">With entities to machine-readable structures for AI Visibility<\/a><\/p>\n<p><a href=\"https:\/\/norbert-kathriner.ch\/en\/structure-beats-ranking-architectural-principles-for-ai-visibility-beyond-seo\/\">Structure beats ranking: architectural principles for AI visibility beyond SEO<\/a><\/p>\n<p><a href=\"https:\/\/norbert-kathriner.ch\/en\/the-geo-trap\/\">The GEO trap<\/a><\/p>\n[divider line_type=&#8221;full-width&#8221; custom_height=&#8221;15&#8243;][\/vc_column_text][divider line_type=&#8221;No Line&#8221; custom_height=&#8221;30&#8243;][vc_column_text css=&#8221;&#8221; text_direction=&#8221;default&#8221;]<strong>AI Visibility offer<\/strong><\/p>\n<p><a href=\"https:\/\/norbert-kathriner.ch\/en\/ai-visibility-offer-have-your-visibility-checked-now\/\">Have your visibility checked now<\/a><\/p>\n[divider line_type=&#8221;full-width&#8221; custom_height=&#8221;15&#8243;][\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row type=&#8221;in_container&#8221; full_screen_row_position=&#8221;middle&#8221; column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; scene_position=&#8221;center&#8221; bottom_padding=&#8221;30&#8243; text_color=&#8221;dark&#8221; text_align=&#8221;left&#8221; row_border_radius=&#8221;none&#8221; row_border_radius_applies=&#8221;bg&#8221; overflow=&#8221;visible&#8221; overlay_strength=&#8221;0.3&#8243; gradient_direction=&#8221;left_to_right&#8221; shape_divider_position=&#8221;bottom&#8221; bg_image_animation=&#8221;none&#8221; gradient_type=&#8221;default&#8221; shape_type=&#8221;&#8221;][vc_column column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; column_position=&#8221;default&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221;][vc_row_inner column_margin=&#8221;default&#8221; column_direction=&#8221;default&#8221; column_direction_tablet=&#8221;default&#8221; column_direction_phone=&#8221;default&#8221; text_align=&#8221;left&#8221; row_position=&#8221;default&#8221; row_position_tablet=&#8221;inherit&#8221; row_position_phone=&#8221;inherit&#8221; overflow=&#8221;visible&#8221; pointer_events=&#8221;all&#8221;][vc_column_inner column_padding=&#8221;no-extra-padding&#8221; column_padding_tablet=&#8221;inherit&#8221; column_padding_phone=&#8221;inherit&#8221; column_padding_position=&#8221;all&#8221; top_margin=&#8221;30&#8243; column_element_direction_desktop=&#8221;default&#8221; column_element_spacing=&#8221;default&#8221; desktop_text_alignment=&#8221;default&#8221; tablet_text_alignment=&#8221;default&#8221; phone_text_alignment=&#8221;default&#8221; background_color_opacity=&#8221;1&#8243; background_hover_color_opacity=&#8221;1&#8243; column_backdrop_filter=&#8221;none&#8221; column_shadow=&#8221;none&#8221; column_border_radius=&#8221;none&#8221; column_link_target=&#8221;_self&#8221; overflow=&#8221;visible&#8221; gradient_direction=&#8221;left_to_right&#8221; overlay_strength=&#8221;0.3&#8243; width=&#8221;1\/1&#8243; tablet_width_inherit=&#8221;default&#8221; animation_type=&#8221;default&#8221; bg_image_animation=&#8221;none&#8221; border_type=&#8221;simple&#8221; column_border_width=&#8221;none&#8221; column_border_style=&#8221;solid&#8221; column_padding_type=&#8221;default&#8221; gradient_type=&#8221;default&#8221;]<div class=\"nectar-fancy-ul\" data-list-icon=\"fa fa-link\" data-animation=\"false\" data-animation-delay=\"0\" data-color=\"accent-color\" data-spacing=\"default\" data-alignment=\"left\"> \n<h2><span class=\"ez-toc-section\" id=\"Sources\"><\/span>Sources<span class=\"ez-toc-section-end\"><\/span><\/h2>\n <\/div>[divider line_type=&#8221;No Line&#8221; custom_height=&#8221;10&#8243;][vc_column_text css=&#8221;&#8221; text_direction=&#8221;default&#8221;]The phases and individual steps described in this article were compared with current RAG research, LLM reasoning literature (Synthesis AI 2025, Wei et al. 2022) and knowledge graph integration studies (Frontiers 2025, Nature 2025). The phase structure corresponds to scientific standards, but extends these to include explicit differentiation between parametric (Case L) and hybrid mode (Case L+O) as well as detailed evidence weighting mechanisms.[\/vc_column_text][divider line_type=&#8221;No Line&#8221; custom_height=&#8221;30&#8243;][vc_column_text css=&#8221;&#8221; text_direction=&#8221;default&#8221;]<strong>Lewis et al. 2020 (RAG Foundation) <\/strong><br \/>\n<a href=\"https:\/\/arxiv.org\/abs\/2005.11401\" target=\"_blank\" rel=\"noopener\">https:\/\/arxiv.org\/abs\/2005.11401<\/a><\/p>\n<p><strong>Synthesis AI 2025 (Reasoning Models)<\/strong><br \/>\n<a href=\"https:\/\/synthesis.ai\/2025\/02\/25\/\" target=\"_blank\" rel=\"noopener\">https:\/\/synthesis.ai\/2025\/02\/25\/<\/a><\/p>\n<p><strong>Frontiers 2025 (LLM-KG Fusion)<\/strong><br \/>\n<a href=\"https:\/\/www.frontiersin.org\/\" target=\"_blank\" rel=\"noopener\">https:\/\/www.frontiersin.org\/<\/a><\/p>\n<p><strong>arXiv RAG Survey 2024<\/strong><br \/>\n<a href=\"https:\/\/arxiv.org\/abs\/2410.12837\" target=\"_blank\" rel=\"noopener\">https:\/\/arxiv.org\/abs\/2410.12837<br \/>\n<\/a><\/p>\n<p><strong>Nature KG Construction 2025<\/strong><br \/>\n<a href=\"https:\/\/www.nature.com\/articles\/s41524-025-01540-6\" target=\"_blank\" rel=\"noopener\">https:\/\/www.nature.com\/articles\/s41524-025-01540-6<\/a><\/p>\n[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row]\n","protected":false},"excerpt":{"rendered":"<p>While the GEO industry measures fan-out queries, AI Visibility is only decided by entity recognition and evidence weighting. This article shows step by step how LLMs really construct answers &#8211; and why evidence is more important than reach. <\/p>\n","protected":false},"author":1,"featured_media":15157,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[503,44],"tags":[],"class_list":{"0":"post-15181","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-ai-visibility-en","8":"category-ai-and-automation"},"_links":{"self":[{"href":"https:\/\/norbert-kathriner.ch\/en\/wp-json\/wp\/v2\/posts\/15181","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/norbert-kathriner.ch\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/norbert-kathriner.ch\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/norbert-kathriner.ch\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/norbert-kathriner.ch\/en\/wp-json\/wp\/v2\/comments?post=15181"}],"version-history":[{"count":0,"href":"https:\/\/norbert-kathriner.ch\/en\/wp-json\/wp\/v2\/posts\/15181\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/norbert-kathriner.ch\/en\/wp-json\/wp\/v2\/media\/15157"}],"wp:attachment":[{"href":"https:\/\/norbert-kathriner.ch\/en\/wp-json\/wp\/v2\/media?parent=15181"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/norbert-kathriner.ch\/en\/wp-json\/wp\/v2\/categories?post=15181"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/norbert-kathriner.ch\/en\/wp-json\/wp\/v2\/tags?post=15181"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}