What Entity means
An entity identifies the particular organization, product, person or concept a page describes. Clear attributes and references help distinguish it from other things with the same name.
Resolution happens through declaration (schema), corroboration (verified external profiles), consistency (one description everywhere) and disambiguation (separating you from similarly-named things).
Why it matters for AI search
An unresolved entity undermines everything built on top of it. Content attributed to an ambiguous string carries far less weight, and citation work aimed at an entity models can't resolve mostly evaporates.
It's also the cleanest overlap between AI and classic SEO — the same signals feed knowledge panels, rich results and model resolution.
See it in action
Three companies share a name. Watch a model try to resolve which one you are.
The model can't tell which company you are.
Every piece of content you publish is being attributed to an ambiguous string. This demonstration illustrates a name collision; it does not measure an actual model or establish a requirement for citation.
How to get it right
Making your entity resolvable
- Deploy accurate Organization schema declaring what you do, where, and what you offer
- Build a sameAs graph linking every verified external profile
- Enforce one canonical name format and description across every property
- Actively disambiguate from similarly-named organisations using distinguishing attributes
- Where a knowledge panel exists, review its accuracy and use the provider’s correction process when needed
Entity disambiguation: separating companies with the same name
Disambiguation connects a name to the right organization rather than another company, product or common word. Distinguishing attributes include the category, location, founding details, people and product names.
For example, two companies called Acme can be distinguished by what they sell, their official domains and their locations. Use accurate attributes and legitimate external profiles that refer to the same organization; do not invent details to fill markup.
Compare how search and AI products describe the company, then investigate apparent name collisions. A larger company sharing a name can make separation harder; consistent information does not guarantee a particular result.
Knowledge graphs: entities and their relationships
A knowledge graph represents entities, their attributes and their relationships. A company can be related to its products, founders and locations. The graph is the underlying information structure, not the box displayed on a search results page.
Accurate organization details, consistent naming, relevant structured data and independent references help readers and systems distinguish the company. They do not guarantee admission to a particular search provider’s knowledge graph.
Knowledge panels: the visible search feature
A knowledge panel is an information box a search engine may show for a recognized entity. It can display a name, description, logo, profiles and related facts. A knowledge graph and a knowledge panel are not the same thing.
When a panel exists, an authorized representative may be able to claim it and suggest corrections through the provider’s process. Correct the public information behind an error as well as submitting a suggestion. The provider decides what appears.
A panel is not required to have a useful website or to be cited elsewhere. Do not treat its absence as proof that a company cannot appear in an AI answer.
Common questions
How do I know if we have an entity problem?
Ask a few models to describe your company. Common symptoms: it describes a different company, gets your category wrong, lists products you don't sell, or hedges heavily.
Absence of a knowledge panel for brand searches is another signal, though not conclusive.
Is entity SEO just schema markup?
Schema is one of four components and on its own the weakest. Declaring attributes means little if no external source corroborates them or your own properties contradict each other.
We've seen technically perfect schema that models still couldn't resolve, because every other signal pointed elsewhere.
What if another company shares our name?
Work through attributes rather than name alone — category, location, founding date, key people, product names — and build corroboration tying those to you.
Full separation isn't always achievable against a much larger entity with the same name, and it's worth being honest about that ceiling.
Related concepts
These come up alongside Entity constantly.