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AI Entity Checker — Is Your Brand an Entity, or Just a String?
The shortcut everyone tries first is creating your own Wikidata item. It gets deleted, usually within weeks, because Wikidata requires independent sources — and a deleted item is worse than none, since the deletion log is public. Enter your brand: this checker reads the public knowledge graph — Wikidata and Wikipedia — matches the record to your domain rather than to your name, and shows whether a more famous same-name entity is the one an AI engine will resolve to instead.
Reads the free, public Wikidata API. The domain is used only to pick the right same-name entity. Results reflect the public knowledge-graph footprint, not a private AI index.
What this entity checker checks
The tool resolves your brand against Wikidata and reports its knowledge-graph footprint. It looks outward at the public web, not at your own pages. It cannot see Google’s private Knowledge Graph or a model’s internal memory, so a strong result here is a strong signal, never a guarantee of citation.
- Whether a Wikidata item exists for the brand
- Same-name collisions and whether you are the top match
- Whether a Wikipedia article backs the item, and in which languages
- The official-website statement (P856) and whether it points at your domain
- Instance-of type, description, aliases and completeness
- Linked external profiles — X, LinkedIn, Crunchbase and more
- Google’s private Knowledge Graph (not public)
- A model’s internal representation of you (not observable)
- Live AI citations for a prompt (see the AI Visibility Audit)
- Your on-page schema (see the Schema Validator)
How the score is calculated
Eight weighted checks. No Wikidata item at all caps the score at 10, because nothing else can be measured without one. Everything above that reflects how complete and unambiguous the footprint is.
| Points | Check | What has to be true |
|---|---|---|
| 25 | Wikidata entity exists | A structured knowledge-graph item resolves for the brand. Without it, nothing else can be scored. |
| 20 | Wikipedia article | Wikipedia is the most-cited domain in AI answers. Localised articles score highest. |
| 15 | Disambiguation | Your entity is the top match for the name, not buried under a more famous same-name item. |
| 12 | Official website linked | The item carries an official-website statement (P856) pointing at your domain. |
| 10 | Typed (instance of) | An instance-of statement declares what kind of thing it is — company, software, product. |
| 8 | Description | A short one-line description the engine uses to say what the entity is. |
| 5 | External profiles | Linked social and database identifiers (X, LinkedIn, Crunchbase) that corroborate the entity. |
| 5 | Entity depth | Enough properties (inception, country, founders, industry) for an accurate description. |
Why AI search needs your brand to be an entity
An engine resolves the name before it answers the question. Ask "best X for Y" and the first thing that happens is not retrieval — it is disambiguation: deciding which real-world thing X refers to. Only then does the model look for what to say about it. Wikidata and Wikipedia are the checkable part of that step, because they are the identity layer anyone outside the vendors can actually inspect. A brand with a Wikidata item, a Wikipedia article and a linked official website gives the engine a stable identifier and enough description to keep you apart from everything else spelled the same way. A brand with only a name is a string competing against every other meaning of that word, including the better-documented ones. This is groundwork, not a guarantee: being resolvable does not earn a citation, but not being resolvable makes an accurate one largely accidental.
Common entity gaps and how to fix them
Ordered roughly from most to least severe. The honest thread through all of them: entity presence follows notability, it cannot be faked ahead of it.
- No Wikidata item at all. The knowledge graph has no record of the brand, so the name resolves to every other meaning of the word and to better-documented rivals. Fix: Earn independent, citable coverage first, then create the item. An unsourced Wikidata item gets deleted — notability comes before the entity, not after.
- The name collides with a more famous entity. A generic or shared name (a colour, a common word, a bigger company) outranks you, so an engine asked the bare name surfaces the other one. Fix: Strengthen your entity’s references and completeness so it rises, and use the fuller brand name in your own content and markup.
- Wikidata item but no Wikipedia article. The single richest source AI pulls from is missing. A bare Wikidata item is far weaker than a Wikidata item plus a Wikipedia article. Fix: A Wikipedia article follows notability: earn reliable secondary coverage, and the article becomes possible. Do not create it prematurely.
- No official website on the item (P856). Without the official-website statement, nothing links the entity to your domain, so an engine cannot connect “the entity” to “this site”. Fix: Add the official website property (P856) to the Wikidata item, pointing at your primary domain.
- The entity is untyped. With no instance-of (P31) statement, the item does not say whether it is a company, a product or a piece of software, so it cannot be categorised. Fix: Add an instance-of statement with the precise type — business, software company, mobile app, and so on.
- sameAs / external profiles missing. No linked X, LinkedIn or Crunchbase identifiers means nothing corroborates the entity across independent sources. Fix: Add the official social and database identifiers to the item; they reinforce how confidently an engine resolves you.
- A thin, near-empty item. An item with a label and little else is easy to distrust or describe wrongly. Sparse entities produce vague or inaccurate AI descriptions. Fix: Add inception date, country, founders, industry, headquarters and logo — the facts that let an engine describe you correctly.
- Chasing an entity before you are notable. Creating a Wikidata item or Wikipedia article with no independent sourcing backfires: it is deleted, and repeated attempts can attract scrutiny. Fix: Build the citation base — press, references, third-party coverage — first. The entity is the consequence of notability, not a shortcut to it.
How to build entity presence the right way
There is no shortcut, and pretending otherwise gets items deleted. The order that works:
- Earn independent coverage. Press, industry references, third-party mentions that are not your own. This is the notability that everything else rests on.
- Get your on-page identity right. A complete Organization entity with
sameAslinks, checked with the Schema Validator, so the web agrees on who you are. - Create a sourced Wikidata item. Once notable, add the item with references, the official website (P856), an instance-of type and a description.
- Let the Wikipedia article follow. When the coverage supports it, an article becomes defensible. Do not write it for yourself prematurely.
- Add corroborating profiles. Link official social and database identifiers so independent sources agree.
Steps one and two you control directly today. The knowledge-graph steps are the consequence of doing them well, not a substitute.
Does a Wikidata entity guarantee AI citations?
No. A complete, unambiguous entity makes your brand resolvable and harder to confuse with others — a prerequisite for being described accurately in an AI answer. Whether an engine actually cites you still depends on content quality, authority, relevance and each engine’s own retrieval. The entity removes an ambiguity blocker so you can be described correctly; being chosen as a cited source is a separate, competitive problem. That gap is what the AI Visibility Audit measures and what AEO and GEO work on.
AI entity checker — FAQ
Why does Wikidata matter for AI visibility?
It is the largest openly licensed, machine-readable identity graph, it is a documented training and grounding source, and it is one of the few knowledge bases anyone outside the vendors can inspect or contribute to. That combination is why it is the practical proxy for "does a machine have a record of this brand" — not because Wikidata itself is queried at answer time.
How do I create a Wikidata item for my brand?
The safe answer is that you do not, at least not first. Wikidata requires each claim to be supported by an independent source, and items created by their own subject without that support are nominated for deletion and usually removed. Earn two or three pieces of substantive independent coverage, then create the item and cite them. A deleted item is worse than no item — the deletion discussion is public and permanent.
How do I get a Wikipedia article?
By being written about, not by writing. Wikipedia's notability bar for companies asks for significant coverage in multiple independent, reliable sources that are not press releases, interviews or paid placements. There is no shortcut, agencies that sell one are selling a page that gets deleted, and the deletion sticks to the brand name afterwards.
My brand name is generic — what can I do?
Stop competing for the bare name and make the qualified form resolvable instead: brand plus category, brand plus city, brand plus the thing you sell. Use that pairing consistently in your own markup, your sameAs targets and your bios. You will not out-rank a famous namesake for the plain string, and you do not need to — you need the qualified query to land on you unambiguously.
What is the P856 property?
Official website. It is the property this checker matches on, because it is the only reliable way to tell your company from a same-named one: names collide, domains do not. An item without P856 cannot be tied to your site by any automated consumer, which is why a missing P856 is reported as an error rather than a warning.
What does "instance of" (P31) mean?
The type declaration — business, software company, non-profit, person. It is what turns a record into something a machine can reason about, and an item without it is a name with links attached. It is also the cheapest gap to close on an item that already exists.
Does this see Google's Knowledge Graph?
No, and neither does any other public tool. Google's graph, and the internal graphs at OpenAI and Anthropic, are not queryable. This checks the public layer they are known to draw on. Anything advertised as your "Knowledge Graph score" is inferred from public signals in exactly this way, whether or not the vendor says so.
Do I need entities in more than one language?
If you sell in more than one language, yes — but as labels and descriptions on one item, not as separate items. A Ukrainian label and description on the same Q-number is a small edit with real effect; a second item for the Ukrainian spelling of your name is a duplicate that will be merged or deleted.
Can having the wrong entity data hurt me?
Yes, and more quietly than having none. A stale address, a wrong founding year or an outdated parent company gets repeated by anything that trusts the record, and corrections propagate slowly. The failure is not that you are absent — it is that you are present and wrong, which reads as authoritative.
How often should I check my entity footprint?
After anything that changes the facts — rebrand, domain move, funding, a merger — and otherwise once or twice a year. Wikidata is openly editable, so records drift without anyone telling you, and the cost of a check is one query.
An entity makes you describable. It does not make you cited.
The free AI Visibility Audit runs 47 checks across crawler access, llms.txt, JSON-LD, metadata, content extractability and current AI visibility alongside your entity footprint, then emails a 0–100 score with the three fixes that move it most.
Run the free AI Visibility Audit