What is Entity SEO for LLMs?
Entity SEO makes an organisation identifiable without ambiguity across its pages and public references. Its name, activity, products and official links should distinguish it from namesakes. This is a matter of data quality and editorial clarity, with no universal entity-clarity score or guaranteed citation.
A generated answer may use learned knowledge and, depending on the product, documents retrieved from the Web. These mechanisms are different. Editing a website does not directly change a model's parameters or guarantee inclusion in its next training run.
Documented guidance and hypotheses
Google explains that Organization structured data can help it identify an organisation. Its AI features guidance retains SEO fundamentals without requiring special AI markup. This does not establish that every LLM uses the same entity graph or assigns the same weight to an external profile.
The seven operational actions
1. Describe the organisation using verifiable facts
Record its official name, genuine trading names, activity and canonical website. Add location, founders or founding date only when public and verified. Describe and date a real business change instead of hiding it to create an appearance of stability.
2. Declare Organization on a relevant page
Use JSON-LD consistent with visible content on the homepage or an appropriate institutional page. Keep a stable identifier. name, url and an accurate description are preferable to invented properties. Repeating the object on every page is not a universal requirement.
3. Link genuine profiles with sameAs
sameAs refers to another URL identifying the same entity. Verify every profile before including it. A page about another company, a fictitious Wikidata identifier or an unrelated social account is not equivalent. Alternative names can be legitimate; explain them instead of assuming every variation damages rankings.
4. Check eligibility before contributing to Wikidata
Wikidata has its own notability criteria. An entity may qualify when clearly identifiable in serious public references or when it serves a structural need. A commercial listing is not automatically eligible. Check duplicates and community rules; do not create an item solely for an expected SEO gain.
5. Earn useful, attributed references
A reproducible study, technical correction or documented dataset provides a concrete reason to cite your work. Assess the citing page's relevance, contextual accuracy and independence. No universal weighting proves that a mention in a large publication always outweighs specialist analysis.
6. Resolve contradictions across channels
Compare your website with profiles you control. Correct an address or product description that has become false. For third-party sources, document the discrepancy before requesting a correction. There is no need to repeat identical wording everywhere when the underlying facts agree.
7. Make editorial responsibility visible
An About page should explain who publishes, what they cover and how to report an error. Display references, partnerships or results only when substantiated. Our editorial team page and corrections register show the institutional information actually available.
Measure disambiguation without inventing a score
Prepare fixed questions about the brand's identity, activity and namesakes. For each answer, record the engine, version if available, language, date, citations and factual errors. Repeat the tests: one correct answer does not establish lasting recognition.
Track brand queries in Search Console, verified external mentions and panel citations separately. A Knowledge Panel describes a Google display, not recognition across all AI engines. Keep missing answers and contradictory sources in the record.
How long should results take?
No result timeline applies to every brand. Crawling, indexing, source selection and training are different stages. A correction may be accessible on your website before it is reflected elsewhere. Verify publication and technical access first, then observe the same questions over time without automatically attributing a change to your intervention.
Explicit translation of the French edition reviewed on 8 September 2026. For markup properties, see the Schema.org guide.