Entity authority is how confident Google and the AI engines are that they know who an entity is and that what they know is reliable. It is built from the strength and consistency of the signals that define the entity: a Wikipedia article where notability supports one, an accurate and well-linked Wikidata entry, an official site marked with the right schema, presence in authoritative directories, and citations from credible third parties that describe the entity consistently. The key word is consistency – when the name, description, and key attributes match across all of these, confidence rises; when they conflict, the systems hedge and the entity becomes fuzzy. Building authority is therefore as much about alignment as accumulation: a coherent set of well-sourced, mutually reinforcing signals beats a scattered pile of mentions. We build this deliberately as the entity layer, because high entity authority is what earns an accurate Knowledge Panel and a confident, correct answer from the AI engines rather than a hedged or mistaken one.

A Knowledge Panel claim lets a verified representative manage a limited set of fields directly and suggest corrections to the rest, but it is important to understand its boundaries. Claiming gives control over things like the logo, official social links, and contact information, and it provides a channel to flag inaccuracies. What it does not do is let you rewrite the substantive facts, because the panel’s core content is generated from the sources behind it – chiefly Wikipedia and Wikidata. If the panel says something wrong about a company’s founding date, leadership, or description, the fix is at the source: correcting the Wikipedia article through disclosed conflict-of-interest editing or updating the Wikidata entry, after which the panel updates. The claim is a useful but shallow lever; the durable control over a Knowledge Panel comes from managing the entity layer underneath it. We monitor those source pages, Wikipedia in particular, with WikiAlerts™, since a change there is what actually moves the panel.

An entity home is the single authoritative web property that an entity controls and that everything else points to as the definitive source of its identity – typically the corporate website or a personal site, marked with the appropriate schema. Every brand and notable person needs one because entity recognition depends on having a stable anchor. When the official site carries clean Organization or Person schema and the entity’s other signals – social profiles, directory listings, Wikidata, press – link back to it through sameAs and consistent description, Google and the AI engines can resolve all those scattered references to one identity with high confidence. Without a clear entity home, the references float, the systems hedge on which ‘thing’ they describe, and the entity stays fuzzy. The entity home is also where the canonical description lives, the version every other property should match. We build and schema-mark the entity home first in most engagements, because it is the foundation the rest of the entity layer attaches to.

Entity resolution is how Google and the AI engines determine that a scatter of references – a LinkedIn profile, a Wikipedia article, a press mention, a directory listing – all refer to the same person or organization. It matters enormously for reputation because resolution is what turns disconnected mentions into a coherent, recognized entity. When resolution succeeds, the systems aggregate everything they know into one confident profile; when it fails, the same person can be split into two half-formed entities, or merged with a namesake, and the resulting Knowledge Panel and AI answers are wrong or thin. Reliable resolution depends on three things: sameAs structured links that explicitly connect the canonical entity home to its other authoritative profiles, consistent attributes (name, role, description) across those references, and strong authoritative anchors like Wikipedia and Wikidata. We build these deliberately, because most entity problems we diagnose are resolution failures – the right signals exist but the systems cannot confidently tie them together.

Entity optimization for a person and for a company share the same logic but draw on different signal sets, so the work is sequenced differently. For a person, the anchors are a bio site marked with Person schema, a complete LinkedIn profile, authoritative bio citations across press and association content, Wikipedia where genuinely notable, and sameAs links tying them together. The emphasis is on consistency of the bio across every credible reference, since people get split or confused easily. For a company, the anchors are the corporate site with Organization schema, an accurate Wikidata entry, Wikipedia where notable, business references like Crunchbase and Bloomberg, and consistent corporate descriptions across directories and press. Companies more often need Wikidata and structured business-directory work; people more often need bio consistency and disambiguation. In both cases the goal is the same – high entity confidence and accurate resolution – but the checklist differs, and we scope the entity layer to the kind of entity we are building.

Google disambiguates entities that share a name by assembling enough context to be confident which one a query means, and the reputation work is about supplying that context cleanly. Several mechanisms combine. Contextual signals – the industry, location, role, and associated topics that consistently appear around the entity – help Google place it. Structured data, especially sameAs links from a schema-marked entity home, explicitly ties an identity to its authoritative profiles. Wikipedia disambiguation pages and distinct articles separate same-named subjects. And Wikidata assigns a unique identifier that gives each entity an unambiguous anchor regardless of name overlap. When these signals are strong and consistent, the systems resolve confidently; when they are thin, two same-named people get conflated or one gets fragmented. For a client with a common name or a namesake, we build dedicated, schema-marked owned properties and distinct authoritative citations to give Google the context it needs, and verify the result with AIQ™.

You can test whether Google recognizes a company as an entity by examining what the systems actually return, since recognition reveals itself in the output. The most direct signal is a Knowledge Panel for the company name – its presence means Google has resolved the entity with enough confidence to display it, and its accuracy tells you the quality of the underlying signals. Beyond that, query Wikidata for the entity to confirm a clean, linked entry exists, since that is one of Google’s primary entity sources. Review how the AI engines describe the company across ChatGPT, Gemini, Perplexity, and Copilot, because confident, accurate, consistent answers indicate strong recognition while hedged, wrong, or conflated answers indicate weak signals or a resolution failure. We run exactly this check with AIQ as a standard diagnostic, comparing how each model describes the entity, and we cross-reference the search layer with IMPACT™, because the gap between what the company believes about itself and what the systems return is usually where the entity work begins.

Entity optimization refers to the process of helping search engines like Google and AI Answer Engines like ChatGPT understand who or what a client is, based on structured signals from across the web. This includes Wikidata entries, schema markup, consistent NAP (name/address/phone) data, authoritative backlinks, and alignment between how a client is described across Wikipedia, owned web properties, social profiles, and other reference sources. A well-optimized entity is more likely to receive a Knowledge Panel and to appear accurately in AI-generated summaries.

The Knowledge Graph is Google’s structured map of the world: a database of distinct entities and the relationships between them, rather than a list of pages. When Google is confident enough about an entity – who a person is, what a company does, how they connect to other entities – it draws on the Graph to generate the Knowledge Panel, populate AI Overviews, and power the entity features that increasingly frame a search result before the user reads a single link. For reputation work this matters because the Graph is upstream of so much of what people see. It is fed by sources Google trusts to define entities: Wikipedia, Wikidata, official websites with schema markup, and authoritative third-party references. Influencing what the Graph believes about an entity means improving those underlying sources, which is the entity layer of a reputation program. We track how an entity renders across Google with IMPACT™, because the Knowledge Graph’s read on a client is now the foundation the rest of the result is built on.

An entity is a thing that search and AI systems recognize as a distinct, identifiable subject with its own attributes and relationships, as opposed to a keyword, which is just a string of characters. The distinction is the foundation of modern reputation work. ‘Apple’ the company is an entity with a CEO, a headquarters, products, and a Wikipedia article; ‘apple’ the fruit is a different entity; the word ‘apple’ is a keyword that could mean either. Google and the AI engines resolve which entity a query refers to, then assemble what they know about that specific node to answer. This is why reputation has shifted from optimizing for keywords to building entity recognition: the goal is for Google and the AI engines to know, with high confidence, who or what a client is, what attributes attach to them, and which other entities they relate to. We call this layer of the work the entity layer, and getting it right is what makes everything downstream – Knowledge Panel, AI summary, accurate disambiguation – possible.