{ "version": "1.0", "schema": "https://entitymap.org/spec/v1.0", "profile": "core", "publisher": { "name": "Hartzer.it.com", "url": "https://hartzer.it.com/" }, "generated": "2026-08-29T00:00:00Z", "verificationStatus": "self-declared", "entities": [ { "entityId": "e_google_ai_overviews", "@type": "DefinedTerm", "name": "AI Overviews", "alternateName": "Google's generated answer block inside Search", "description": "The best-documented AI search surface, and the gap between what Google states and what practitioners sell.", "hasChunks": [ { "chunkId": "c_google-ai-overviews", "text": "AI Overviews is a feature of Google Search that places a generated summary at or near the top of the results page for some queries. The summary is written by a Gemini model working alongside Google's ranking systems and the Knowledge Graph, and it carries links out to pages that Google says support the statements it makes. Nobody opts in per query.", "sourceUrl": "https://hartzer.it.com/google-ai-overviews/", "pageTitle": "Google AI Overviews: What the Evidence Shows | Hartzer.it.com", "publisher": "Hartzer.it.com", "contentType": "definition" }, { "chunkId": "c_google-ai-overviews_verdict", "text": "Documented. Google documents the model, the eligibility rule and the controls; which eligible pages get cited it has never documented.", "sourceUrl": "https://hartzer.it.com/google-ai-overviews/", "pageTitle": "Google AI Overviews: What the Evidence Shows | Hartzer.it.com", "publisher": "Hartzer.it.com", "contentType": "claim" } ], "relations": [ { "predicate": "PART_OF", "target": "e_grp_surface", "targetId": "e_grp_surface", "targetName": "AI Search Surfaces" } ], "sameAs": "https://en.wikipedia.org/wiki/AI_Overviews" }, { "entityId": "e_google_ai_mode", "@type": "DefinedTerm", "name": "AI Mode", "alternateName": "Google's conversational search surface", "description": "Google's conversational search tab, the fan-out behind it, and why its citations barely overlap with AI Overviews.", "hasChunks": [ { "chunkId": "c_google-ai-mode", "text": "AI Mode is a conversational search surface inside Google Search: rather than returning a ranked list of results, it returns a written answer assembled from several searches Google runs on the user's behalf, and the thread stays open for follow-up questions. Users reach it from a tab inside Search; it does not replace the ordinary results page. It is a different product from AI Overviews , which is a summary block on that ordinary page, and from the Gemini app, which sits outside Search altogether.", "sourceUrl": "https://hartzer.it.com/google-ai-mode/", "pageTitle": "Google AI Mode: What Is Documented, What Is Not | Hartzer.it.com", "publisher": "Hartzer.it.com", "contentType": "definition" }, { "chunkId": "c_google-ai-mode_verdict", "text": "Observed. Google names the mechanism and publishes none of its output; what is known about AI Mode citation comes from vendor measurement.", "sourceUrl": "https://hartzer.it.com/google-ai-mode/", "pageTitle": "Google AI Mode: What Is Documented, What Is Not | Hartzer.it.com", "publisher": "Hartzer.it.com", "contentType": "claim" } ], "relations": [ { "predicate": "PART_OF", "target": "e_grp_surface", "targetId": "e_grp_surface", "targetName": "AI Search Surfaces" } ] }, { "entityId": "e_chatgpt_search", "@type": "DefinedTerm", "name": "ChatGPT search", "alternateName": "OpenAI's web retrieval layer inside ChatGPT", "description": "The web retrieval layer inside ChatGPT: well documented as plumbing, entirely undocumented as a ranking system.", "hasChunks": [ { "chunkId": "c_chatgpt-search", "text": "ChatGPT search is the web retrieval layer inside OpenAI's ChatGPT assistant. When a prompt looks as though it needs current or external information, ChatGPT issues queries against search providers, reads a small set of the pages that come back, and writes an answer carrying inline links to the sources it used.", "sourceUrl": "https://hartzer.it.com/chatgpt-search/", "pageTitle": "ChatGPT Search: Crawlers, Citations, Evidence | Hartzer.it.com", "publisher": "Hartzer.it.com", "contentType": "definition" }, { "chunkId": "c_chatgpt-search_verdict", "text": "Unsupported. OpenAI documents its crawlers and its referral tag in detail, and publishes nothing at all about how sources are chosen.", "sourceUrl": "https://hartzer.it.com/chatgpt-search/", "pageTitle": "ChatGPT Search: Crawlers, Citations, Evidence | Hartzer.it.com", "publisher": "Hartzer.it.com", "contentType": "claim" } ], "relations": [ { "predicate": "PART_OF", "target": "e_grp_surface", "targetId": "e_grp_surface", "targetName": "AI Search Surfaces" } ], "sameAs": "https://en.wikipedia.org/wiki/ChatGPT" }, { "entityId": "e_perplexity", "@type": "DefinedTerm", "name": "Perplexity", "alternateName": "The answer engine where citation is the product", "description": "The answer engine whose crawler documentation is exact, whose source selection is undocumented, and whose blocking advice is usually wrong.", "hasChunks": [ { "chunkId": "c_perplexity", "text": "Perplexity AI is an answer engine: a search product whose default output is written prose rather than a list of results. A query triggers a live search of the web, the system reads the pages it retrieves, and it returns an answer carrying numbered inline citations, each one a link, with a strip of sources beside or above the text.", "sourceUrl": "https://hartzer.it.com/perplexity/", "pageTitle": "Perplexity: How It Cites, Crawls and Pays | Hartzer.it.com", "publisher": "Hartzer.it.com", "contentType": "definition" }, { "chunkId": "c_perplexity_verdict", "text": "Unsupported. Perplexity's own documentation contradicts the standard advice for blocking it, and says nothing about how sources are chosen.", "sourceUrl": "https://hartzer.it.com/perplexity/", "pageTitle": "Perplexity: How It Cites, Crawls and Pays | Hartzer.it.com", "publisher": "Hartzer.it.com", "contentType": "claim" } ], "relations": [ { "predicate": "PART_OF", "target": "e_grp_surface", "targetId": "e_grp_surface", "targetName": "AI Search Surfaces" } ], "sameAs": "https://en.wikipedia.org/wiki/Perplexity_AI" }, { "entityId": "e_google_gemini", "@type": "DefinedTerm", "name": "Gemini", "alternateName": "Google's answer app, outside Search and unreported", "description": "The answer surface Google documents least, and the one every optimization claim is guessing about.", "hasChunks": [ { "chunkId": "c_google-gemini", "text": "Gemini is Google's family of generative AI models and the consumer app built on them; treated as a search surface, it is a place where a person asks a question and receives a written answer that may, but need not, carry links to web pages. Those links come from grounding — at answer time the model issues queries against the Google Search index, retrieves current pages, and attaches them to its response as related sources, which is what separates an answer reflecting the live web from one composed entirely out of model weights.", "sourceUrl": "https://hartzer.it.com/google-gemini/", "pageTitle": "Google Gemini as a Search and Answer Surface | Hartzer.it.com", "publisher": "Hartzer.it.com", "contentType": "definition" }, { "chunkId": "c_google-gemini_verdict", "text": "Unsupported. Google publishes no eligibility rule, no selection criteria and no reporting for Gemini, so every lever sold for it is inferred.", "sourceUrl": "https://hartzer.it.com/google-gemini/", "pageTitle": "Google Gemini as a Search and Answer Surface | Hartzer.it.com", "publisher": "Hartzer.it.com", "contentType": "claim" } ], "relations": [ { "predicate": "PART_OF", "target": "e_grp_surface", "targetId": "e_grp_surface", "targetName": "AI Search Surfaces" } ] }, { "entityId": "e_microsoft_copilot", "@type": "DefinedTerm", "name": "Microsoft Copilot", "alternateName": "Bing-grounded answers with real publisher reporting", "description": "Bing-grounded generative answers, page-level publisher controls, and the only citation-impression reporting in the field.", "hasChunks": [ { "chunkId": "c_microsoft-copilot", "text": "Microsoft Copilot is Microsoft's consumer AI assistant, and Copilot Search is the generative answer layer inside Bing. Both are grounded on the Bing index: Bing performs the crawl and the retrieval, and a language model writes an answer over the retrieved passages with inline links back to the sources. The name has moved twice. It launched as Bing Chat on 7 February 2023, became Copilot on 15 November 2023, and the brand now covers a consumer assistant, Copilot inside Windows and Microsoft 365, and Copilot Studio for building enterprise agents.", "sourceUrl": "https://hartzer.it.com/microsoft-copilot/", "pageTitle": "Microsoft Copilot: Citations and Bing Reporting | Hartzer.it.com", "publisher": "Hartzer.it.com", "contentType": "definition" }, { "chunkId": "c_microsoft-copilot_verdict", "text": "Observed. Microsoft reports citations to publishers better than any other operator, and still publishes no criteria for what gets cited.", "sourceUrl": "https://hartzer.it.com/microsoft-copilot/", "pageTitle": "Microsoft Copilot: Citations and Bing Reporting | Hartzer.it.com", "publisher": "Hartzer.it.com", "contentType": "claim" } ], "relations": [ { "predicate": "PART_OF", "target": "e_grp_surface", "targetId": "e_grp_surface", "targetName": "AI Search Surfaces" } ], "sameAs": "https://en.wikipedia.org/wiki/Microsoft_Copilot" }, { "entityId": "e_ai_assistants_and_smaller_engines", "@type": "DefinedTerm", "name": "AI assistants and smaller engines", "alternateName": "Claude, Meta AI, DuckDuckGo, Brave and Grok", "description": "The assistants and engines too small to warrant a page each, compared on the only thing that separates them: what they document.", "hasChunks": [ { "chunkId": "c_ai-assistants-and-smaller-engines", "text": "An AI assistant, in the sense this page uses, is a conversational product that answers a question by fetching live web pages and, usually, showing the reader where the answer came from. This page covers the ones that are not ChatGPT, not Google and not Microsoft: Claude from Anthropic, Meta AI, DuckDuckGo's AI-assisted answers, Brave Search, Grok from xAI, and Le Chat from Mistral. They are grouped because none of them individually justifies a page, and pretending otherwise would misrepresent the evidence.", "sourceUrl": "https://hartzer.it.com/ai-assistants-and-smaller-engines/", "pageTitle": "AI Assistants: Claude, Meta AI, DuckDuckGo, Brave", "publisher": "Hartzer.it.com", "contentType": "definition" }, { "chunkId": "c_ai-assistants-and-smaller-engines_verdict", "text": "Unsupported. Not one of these operators documents how it selects sources, and not one documents how a publisher should measure it.", "sourceUrl": "https://hartzer.it.com/ai-assistants-and-smaller-engines/", "pageTitle": "AI Assistants: Claude, Meta AI, DuckDuckGo, Brave", "publisher": "Hartzer.it.com", "contentType": "claim" } ], "relations": [ { "predicate": "PART_OF", "target": "e_grp_surface", "targetId": "e_grp_surface", "targetName": "AI Search Surfaces" } ] }, { "entityId": "e_generative_engine_optimization", "@type": "DefinedTerm", "name": "Generative Engine Optimization", "alternateName": "What GEO claims and what the evidence supports", "description": "The founding paper, the misquoted 40%, and the replication attempt that found three significant results out of fifty-four.", "hasChunks": [ { "chunkId": "c_generative-engine-optimization", "text": "Generative engine optimization (GEO) is the practice of changing a web page, or a body of content, so that a generative AI system — one that answers a question in prose and attaches citations, such as Google AI Overviews, Google AI Mode, ChatGPT search, Perplexity or Microsoft Copilot — becomes more likely to retrieve it, cite it, or repeat its claims. That is the definition the term was born with, and it is narrower than the one sold.", "sourceUrl": "https://hartzer.it.com/generative-engine-optimization/", "pageTitle": "Generative Engine Optimization: The Evidence | Hartzer.it.com", "publisher": "Hartzer.it.com", "contentType": "definition" }, { "chunkId": "c_generative-engine-optimization_verdict", "text": "Unsupported. The term and the paper behind it are real; the content tactics sold under the name fail almost every independent test run on them.", "sourceUrl": "https://hartzer.it.com/generative-engine-optimization/", "pageTitle": "Generative Engine Optimization: The Evidence | Hartzer.it.com", "publisher": "Hartzer.it.com", "contentType": "claim" } ], "relations": [ { "predicate": "PART_OF", "target": "e_grp_technique", "targetId": "e_grp_technique", "targetName": "Techniques and Signals" } ], "sameAs": "https://en.wikipedia.org/wiki/Generative_engine_optimization" }, { "entityId": "e_answer_engine_optimization", "@type": "DefinedTerm", "name": "Answer Engine Optimization", "alternateName": "Whether AEO is a discipline or a label", "description": "The term predates generative AI, the tactic it was built on has been withdrawn, and no operator documents it under that name.", "hasChunks": [ { "chunkId": "c_answer-engine-optimization", "text": "Answer engine optimization (AEO) is the practice of structuring content so that a system returning a single direct answer — rather than a list of blue links — uses that content as the answer. An answer engine, in this sense, is any product that collapses a result set into one response: a voice assistant reading a result aloud, a featured snippet, or in 2026 an AI assistant writing a paragraph with citations attached. The term predates generative AI by roughly five years.", "sourceUrl": "https://hartzer.it.com/answer-engine-optimization/", "pageTitle": "Answer Engine Optimization: Is AEO Distinct? | Hartzer.it.com", "publisher": "Hartzer.it.com", "contentType": "definition" }, { "chunkId": "c_answer-engine-optimization_verdict", "text": "Unsupported. No operator documents AEO, Wikipedia redirects it to GEO, and a vendor selling it says in print the two describe one job.", "sourceUrl": "https://hartzer.it.com/answer-engine-optimization/", "pageTitle": "Answer Engine Optimization: Is AEO Distinct? | Hartzer.it.com", "publisher": "Hartzer.it.com", "contentType": "claim" } ], "relations": [ { "predicate": "PART_OF", "target": "e_grp_technique", "targetId": "e_grp_technique", "targetName": "Techniques and Signals" } ] }, { "entityId": "e_geo_vs_seo", "@type": "DefinedTerm", "name": "GEO vs AEO vs SEO", "alternateName": "How the three terms relate, overlap and differ", "description": "Where each acronym came from, who gained by pushing it, what actually changed about the surface, and the split verdict on whether classic SEO is enough.", "hasChunks": [ { "chunkId": "c_geo-vs-seo", "text": "Search engine optimization (SEO) is the long-established practice of making a page findable and rankable in a search index; it has been in documented use since 1997 and predates Google. Generative engine optimization (GEO) comes from a 2023 academic preprint and names the practice of influencing what a generative model says and cites. Answer engine optimization (AEO) is older than GEO, in circulation by early 2018 for voice search and featured snippets, and has since been re-pointed at the same AI surfaces GEO addresses.", "sourceUrl": "https://hartzer.it.com/geo-vs-seo/", "pageTitle": "GEO vs SEO vs AEO: What Actually Differs | Hartzer.it.com", "publisher": "Hartzer.it.com", "contentType": "definition" }, { "chunkId": "c_geo-vs-seo_verdict", "text": "Unsupported. Sold as three disciplines, the split has no evidence: no operator documents anything that answers to one label and not the others.", "sourceUrl": "https://hartzer.it.com/geo-vs-seo/", "pageTitle": "GEO vs SEO vs AEO: What Actually Differs | Hartzer.it.com", "publisher": "Hartzer.it.com", "contentType": "claim" } ], "relations": [ { "predicate": "PART_OF", "target": "e_grp_technique", "targetId": "e_grp_technique", "targetName": "Techniques and Signals" } ], "sameAs": "https://en.wikipedia.org/wiki/Search_engine_optimization" }, { "entityId": "e_ai_citations_and_source_selection", "@type": "DefinedTerm", "name": "AI Citations and Source Selection", "alternateName": "How AI answers pick and credit their sources", "description": "The eligibility rule is documented and the selection function is not, which is why four studies of the same question produced four different numbers.", "hasChunks": [ { "chunkId": "c_ai-citations-and-source-selection", "text": "Source selection is not one step. An AI answer surface decides which pages to retrieve , which of those to actually use when composing the answer, and which to show the reader as a citation — a visible attribution, usually a clickable link, beneath or beside the generated text. Those three sets are not the same set. A page can be retrieved and never used, used and never cited, or cited while contributing almost nothing to the sentence it sits under.", "sourceUrl": "https://hartzer.it.com/ai-citations-and-source-selection/", "pageTitle": "AI Citations and Source Selection | Hartzer.it.com", "publisher": "Hartzer.it.com", "contentType": "definition" }, { "chunkId": "c_ai-citations-and-source-selection_verdict", "text": "Observed. Eligibility for citation is documented; how sources are chosen and credited is not, and the published figures contradict each other.", "sourceUrl": "https://hartzer.it.com/ai-citations-and-source-selection/", "pageTitle": "AI Citations and Source Selection | Hartzer.it.com", "publisher": "Hartzer.it.com", "contentType": "claim" } ], "relations": [ { "predicate": "PART_OF", "target": "e_grp_technique", "targetId": "e_grp_technique", "targetName": "Techniques and Signals" } ], "sameAs": "https://en.wikipedia.org/wiki/Citation" }, { "entityId": "e_brand_mentions_in_ai_search", "@type": "DefinedTerm", "name": "Brand Mentions in AI Search", "alternateName": "Whether unlinked mentions move AI answers", "description": "Two different mechanisms get sold as one. One follows from documented retrieval behavior; the other is an inference about training data that nobody outside the labs can test.", "hasChunks": [ { "chunkId": "c_brand-mentions-in-ai-search", "text": "A brand mention is any occurrence of a brand's name — the name a company trades under, and the associations attached to it — in text on a page that brand does not control. An unlinked mention is one with no hyperlink pointing back: the name appears, nothing points anywhere. Under classic SEO an unlinked mention was a curiosity, something to convert into a link. Since 2025 it has been sold as the primary input to AI search visibility, on the reasoning that language models consume text rather than link graphs.", "sourceUrl": "https://hartzer.it.com/brand-mentions-in-ai-search/", "pageTitle": "Brand Mentions in AI Search | Hartzer.it.com", "publisher": "Hartzer.it.com", "contentType": "definition" }, { "chunkId": "c_brand-mentions-in-ai-search_verdict", "text": "Unsupported. Mentions and AI visibility move together in every study; no published experiment shows that adding mentions moves anything.", "sourceUrl": "https://hartzer.it.com/brand-mentions-in-ai-search/", "pageTitle": "Brand Mentions in AI Search | Hartzer.it.com", "publisher": "Hartzer.it.com", "contentType": "claim" } ], "relations": [ { "predicate": "PART_OF", "target": "e_grp_technique", "targetId": "e_grp_technique", "targetName": "Techniques and Signals" } ], "sameAs": "https://en.wikipedia.org/wiki/Brand_awareness" }, { "entityId": "e_schema_markup_for_ai_search", "@type": "DefinedTerm", "name": "Schema Markup for AI Search", "alternateName": "What structured data does and does not do", "description": "The correlation between schema and AI citations is real, the causal evidence is null, and Google has written down which half matters.", "hasChunks": [ { "chunkId": "c_schema-markup-for-ai-search", "text": "Structured data is machine-readable metadata added to a web page describing what the page is about — a product, a person, an organization, an event — using a shared vocabulary. On the web that vocabulary is almost always schema.org , launched on 2 June 2011 by Google, Microsoft and Yahoo, and the dominant serialization is JSON-LD (JavaScript Object Notation for Linked Data), a block of JSON placed inside a script tag on the page. Its historical payoff was concrete and visible.", "sourceUrl": "https://hartzer.it.com/schema-markup-for-ai-search/", "pageTitle": "Schema Markup for AI Search: The Evidence | Hartzer.it.com", "publisher": "Hartzer.it.com", "contentType": "definition" }, { "chunkId": "c_schema-markup-for-ai-search_verdict", "text": "Unsupported. Google states no special markup is needed for its AI features, and the one controlled test found no citation uplift anywhere.", "sourceUrl": "https://hartzer.it.com/schema-markup-for-ai-search/", "pageTitle": "Schema Markup for AI Search: The Evidence | Hartzer.it.com", "publisher": "Hartzer.it.com", "contentType": "claim" } ], "relations": [ { "predicate": "PART_OF", "target": "e_grp_technique", "targetId": "e_grp_technique", "targetName": "Techniques and Signals" } ] }, { "entityId": "e_faq_and_answer_extraction", "@type": "DefinedTerm", "name": "FAQ Markup and Answer Extraction", "alternateName": "The markup is dead; the writing pattern is not", "description": "Google ended FAQ rich results on 7 May 2026 with no blog post. The editorial pattern survived the markup, and they were never the same thing.", "hasChunks": [ { "chunkId": "c_faq-and-answer-extraction", "text": "They have different mechanisms and, since May 2026, very different fates. Keeping them apart is most of the work on this subject. FAQPage structured data is schema.org markup declaring that a page contains a list of questions and their answers, each question a Question with an acceptedAnswer . Between 2018 and 2023 it made a page eligible for an FAQ rich result — an expandable accordion attached to the page's listing in Google's results, which visibly enlarged the listing and was widely credited with lifting click-through.", "sourceUrl": "https://hartzer.it.com/faq-and-answer-extraction/", "pageTitle": "FAQ Markup and Answer Extraction in AI Search | Hartzer.it.com", "publisher": "Hartzer.it.com", "contentType": "definition" }, { "chunkId": "c_faq-and-answer-extraction_verdict", "text": "Unsupported. FAQ rich results were removed from Google Search entirely on 7 May 2026; the markup now produces no search appearance at all.", "sourceUrl": "https://hartzer.it.com/faq-and-answer-extraction/", "pageTitle": "FAQ Markup and Answer Extraction in AI Search | Hartzer.it.com", "publisher": "Hartzer.it.com", "contentType": "claim" } ], "relations": [ { "predicate": "PART_OF", "target": "e_grp_technique", "targetId": "e_grp_technique", "targetName": "Techniques and Signals" } ], "sameAs": "https://en.wikipedia.org/wiki/Question_answering" }, { "entityId": "e_content_chunking_and_passage_retrieval", "@type": "DefinedTerm", "name": "Content Chunking and Passage Retrieval", "alternateName": "Retrieval matches spans of text, not URLs", "description": "What chunking and passage retrieval actually are, why passage ranking is a different mechanism, and where the recommended section lengths came from.", "hasChunks": [ { "chunkId": "c_content-chunking-and-passage-retrieval", "text": "Chunking is the step in a retrieval pipeline where a document is cut into smaller units before it is indexed, so the system can return a relevant fragment instead of a whole file. Passage retrieval is the read side of the same idea: matching a query against those fragments and returning the ones that answer it. Both belong to information retrieval , the discipline concerned with finding material that satisfies a need from within a large collection.", "sourceUrl": "https://hartzer.it.com/content-chunking-and-passage-retrieval/", "pageTitle": "Content Chunking and Passage Retrieval in AI Search", "publisher": "Hartzer.it.com", "contentType": "definition" }, { "chunkId": "c_content-chunking-and-passage-retrieval_verdict", "text": "Observed. The retrievable unit really is the passage, but no operator publishes a chunk size and Google says not to fragment your content.", "sourceUrl": "https://hartzer.it.com/content-chunking-and-passage-retrieval/", "pageTitle": "Content Chunking and Passage Retrieval in AI Search", "publisher": "Hartzer.it.com", "contentType": "claim" } ], "relations": [ { "predicate": "PART_OF", "target": "e_grp_technique", "targetId": "e_grp_technique", "targetName": "Techniques and Signals" } ], "sameAs": "https://en.wikipedia.org/wiki/Information_retrieval" }, { "entityId": "e_entity_grounding_and_the_knowledge_graph", "@type": "DefinedTerm", "name": "Entity Grounding and the Knowledge Graph", "alternateName": "Whether being a known entity gets you cited", "description": "Google documents what the Knowledge Graph is and what Organization markup does. Nobody has published a study connecting either to being cited in an AI answer.", "hasChunks": [ { "chunkId": "c_entity-grounding-and-the-knowledge-graph", "text": "An entity is a thing with an identity — a person, a company, a place, a product, an event — as distinct from the string of characters used to name it. Google's framing when it launched the Knowledge Graph in 2012 was \"things, not strings.\" Entity grounding , also called entity linking or entity resolution, is the step in which a system reads a name in text and decides which known thing it refers to: that this \"Apple\" is the company rather than the fruit, and that the surname in one document belongs to the same person as the surname in another.", "sourceUrl": "https://hartzer.it.com/entity-grounding-and-the-knowledge-graph/", "pageTitle": "Entity Grounding and the Knowledge Graph | Hartzer.it.com", "publisher": "Hartzer.it.com", "contentType": "definition" }, { "chunkId": "c_entity-grounding-and-the-knowledge-graph_verdict", "text": "Unsupported. The Knowledge Graph is documented and real; that being in it makes an AI answer cite you has never been measured, either way.", "sourceUrl": "https://hartzer.it.com/entity-grounding-and-the-knowledge-graph/", "pageTitle": "Entity Grounding and the Knowledge Graph | Hartzer.it.com", "publisher": "Hartzer.it.com", "contentType": "claim" } ], "relations": [ { "predicate": "PART_OF", "target": "e_grp_technique", "targetId": "e_grp_technique", "targetName": "Techniques and Signals" } ] }, { "entityId": "e_eeat_signals_in_ai_search", "@type": "DefinedTerm", "name": "E-E-A-T Signals in AI Search", "alternateName": "A rater framework, carried into a silence", "description": "E-E-A-T is the most documented concept in search and the least documented in relation to AI answers. What fills that gap is the old checklist, relabeled.", "hasChunks": [ { "chunkId": "c_eeat-signals-in-ai-search", "text": "E-E-A-T stands for Experience, Expertise, Authoritativeness and Trust. It comes from Google's Search Quality Rater Guidelines , the manual issued to the external human contractors Google pays to rate sample search results. Raters apply it when judging Page Quality. It was E-A-T from 2014; Google added the leading Experience — first-hand or life experience with the subject — on 15 December 2022. What it is not is a score.", "sourceUrl": "https://hartzer.it.com/eeat-signals-in-ai-search/", "pageTitle": "E-E-A-T in AI Search: What Google Documents | Hartzer.it.com", "publisher": "Hartzer.it.com", "contentType": "definition" }, { "chunkId": "c_eeat-signals-in-ai-search_verdict", "text": "Unsupported. No AI operator documents author or credential signals, and Google's May 2026 AI guide never uses the term E-E-A-T.", "sourceUrl": "https://hartzer.it.com/eeat-signals-in-ai-search/", "pageTitle": "E-E-A-T in AI Search: What Google Documents | Hartzer.it.com", "publisher": "Hartzer.it.com", "contentType": "claim" } ], "relations": [ { "predicate": "PART_OF", "target": "e_grp_technique", "targetId": "e_grp_technique", "targetName": "Techniques and Signals" } ] }, { "entityId": "e_ai_crawler_directives_and_llms_txt", "@type": "DefinedTerm", "name": "AI Crawler Directives and llms.txt", "alternateName": "Which tokens matter, and which file nobody reads", "description": "The tokens that cost you AI visibility if you block them, the ones that only touch training, and the server-log study that settles llms.txt.", "hasChunks": [ { "chunkId": "c_ai-crawler-directives-and-llms-txt", "text": "AI crawler directives are entries in a site's robots.txt naming the user-agent tokens that AI companies use, and allowing or disallowing them. 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