Person schema: accurate identity fields and verification
Led growth at cnvrg.io before its acquisition by Intel.
Person schema describes who a page is about. Accurate properties and identity references can support disambiguation; valid markup does not guarantee that an AI system will identify or cite the person correctly.
Use accurate Person markup to describe the intended person, with a stable @id and applicable visible facts. Add identity-equivalent sameAs links that identify this person unambiguously. There is no universal five-field package that makes an LLM trust, resolve or cite someone.
Why disambiguation matters
People with similar names can be confused in search or answers. Review the actual answer, linked source and conflicting facts before deciding whether there is an identity problem. A clear profile and consistent markup support interpretation without proving how an engine resolves the identity.
The minimum viable Person schema
{
"@context": "https://schema.org",
"@type": "Person",
"@id": "https://yanivgoldenberg.com/#person",
"name": "Yaniv Goldenberg",
"url": "https://yanivgoldenberg.com",
"jobTitle": "Fractional CMO/CGO",
"description": "Yaniv Goldenberg is a fractional CMO based in Israel. He scales post-product-market-fit companies to the revenue milestone that unlocks their next funding round. He scaled Elementor from $200K to $20M in ARR (a 100x increase), grew Riverside.fm's Net MRR by 337%, and led demand generation at cnvrg.io, which Intel acquired in 2020. With 10+ years operating across every channel, motion, and stage, he hands the growth engine back once a team can run it without him.",
"image": {
"url": "https://yanivgoldenberg.com/wp-content/uploads/2026/02/Yaniv-Goldenberg-1.jpg",
"@type": "ImageObject",
"width": 800,
"height": 800
},
"sameAs": [
"https://www.linkedin.com/in/goldenbergyaniv/",
"https://x.com/YanivGoldenberg",
"https://github.com/yanivgoldenberg",
"https://www.youtube.com/@YanivGoldenberg-Growth",
"https://www.facebook.com/goldenberg.yaniv",
"https://www.crunchbase.com/person/yaniv-goldenberg",
"https://www.wikidata.org/wiki/Q139976738"
],
"alumniOf": [
{
"url": "https://elementor.com",
"name": "Elementor",
"@type": "Organization"
},
{
"name": "cnvrg.io",
"@type": "Organization"
}
],
"worksFor": {
"url": "https://yanivgoldenberg.com",
"name": "Yaniv Goldenberg",
"@type": "Organization"
}
}Additional applicable fields to consider
- Add knowsAbout: 8-15 specific topics you have demonstrated expertise in
- Add knowsLanguage: every language you operate in professionally
- Add address: at least addressLocality and addressCountry
- Add hasOccupation with full Occupation entity
- Add sameAs links only to reference pages that unambiguously identify this same person, such as verified profiles or an applicable Wikidata or Wikipedia entry. There is no required link quota; a relevant article or podcast mention is not automatically an identity-equivalent page.
- Mirror the Person schema in a visible disambiguation block on the /about/ page
The disambiguation block
On the visible page, explain the person's name, role and distinguishing context using verifiable facts. Keep that explanation consistent with the Person markup and linked profiles. This supports accurate interpretation; a note addressed to AI systems is not an instruction they must follow or proof of correct disambiguation.
What to verify externally
Every claim in your Person schema should have an external source that the LLM can find independently. Your LinkedIn confirms your tenure. Your GitHub confirms your open-source work. A Crunchbase entry confirms your investment or exit history. A podcast appearance confirms your expertise area. The schema is the index; the external sources are the proof.
Frequently asked questions
Do I need a Wikipedia page for entity disambiguation?
Wikipedia is not required. Use a truthful canonical profile and appropriate Person properties, with sameAs links that unambiguously identify the same person. Neither a fixed number of links nor valid markup guarantees disambiguation or citation.
What if a duplicate-name profile is hurting my disambiguation?
Inspect the mistaken answer and its sources, correct inaccurate facts you control and use appropriate identity references for the intended person. A fixed number of links or a stronger presumed signal does not guarantee that an engine will correct its answer.
Should I claim third-party profiles that misrepresent me?
Yes when possible. Some aggregator sites (LinkedIn-clone profiles, AI-generated bio sites) allow you to claim and correct. Worth the effort if those profiles are showing up in search.
What are the minimum fields a Person schema needs?
Schema.org describes available Person properties rather than a universal five-field minimum for LLM citation. Use a stable @id, accurate name and profile URL, and other properties when they truthfully describe the visible person. sameAs should point to identity-equivalent pages, not every article or mention.
How many Person schema nodes should my site have, and where should it live?
One canonical Person node per site, referenced by @id everywhere else. That beats a dozen conflicting inline copies. The canonical Person schema belongs on a dedicated /about/ page. Keep the sameAs accurate and make sure the jobTitle matches your real profiles.
What should the visible disambiguation block on the About page say?
It should explain your name, role and distinguishing context using verifiable facts, and stay consistent with the Person markup and linked profiles. That supports accurate interpretation. A note addressed to AI systems is not an instruction they must follow, and it is not proof of correct disambiguation.
What should I verify externally after adding Person schema?
Every claim in your Person schema should have an external source that an LLM can find independently. Check that the profiles in your sameAs actually exist, match the name and title you claim, and link back where they can. Schema you assert is only as strong as the sources that confirm it.
Published: 2026-04-27 · By Yaniv Goldenberg · Get audited
Person schema for accurate identity description
A clear identity description is useful infrastructure. Its accuracy and consistency can be checked; a resulting citation or correct AI answer must be observed separately.
Start with the person and the facts visible on the profile. Use a stable @id and accurate name and URL, then applicable properties such as role, affiliation or topics. Choose the fields because they describe the person, rather than promising that a five-field bundle removes ambiguity in an AI system.
Add applicable details only when they are accurate and relevant. sameAs points to a reference page that unambiguously identifies the same person; a third-party article is not automatically equivalent. There is no established universal citation weight for the number of links or a particular optional Person property.
Then verify externally, because schema you assert is only as strong as the sources that confirm it. Make sure the profiles in your sameAs actually exist, match the name and title you claim, and link back where they can. One canonical Person node per site, referenced by @id everywhere else, beats a dozen conflicting inline copies. Get the Person schema and its external confirmation aligned, and you have a consistent identity description to inspect. Correct identification and citation in an answer still require their own observations.
Further reading: Schema.org and Named-entity recognition. Related: GEO (AI search), AI visibility audit, More on AI search.
Why Person schema is worth the effort
Accurate Person markup is maintained identity infrastructure. I have observed a founder move from an ambiguous answer to consistent citations within a couple of index cycles, but that observation does not isolate the effect of the markup or establish another person’s timing. Review the actual answer and sources alongside the implementation.
Use external references to verify the facts they actually support. A profile can document a role or identity; it does not automatically establish every company outcome or an engine trust score. Record incorrect answers and their sources so corrections address the observed problem.
Maintain accurate identity markup and references as roles and organizations change. Check visible copy and generated JSON-LD together, and verify the actual answer separately if disambiguation is the buyer problem. This makes the implementation reviewable without promising a citation.
A useful acceptance check is concrete: one intended person has a consistent @id, truthful properties, matching visible facts and appropriate identity links. Keep that implementation result separate from later search or AI-answer behavior.
Treat it as infrastructure you set once and check quarterly, not a task you finish and forget.
Set it once, verify the external profiles, and revisit it whenever your role or company changes so the entity an AI reads always matches the truth.
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Fractional Head of Growth for B2B SaaS, B2C, and e-commerce. Revenue that compounds, not spikes. Any channel, any motion, any stage.
About the author
Yaniv GoldenbergFractional CMO & Head of GrowthFractional CMO & Head of Growth — I lead strategy and personally execute across the revenue funnel. He scales post-product-market-fit companies to the revenue milestone that unlocks their next funding round. He scaled Elementor from $200K to $20M in ARR (a 100x increase), grew Riverside.fm’s Net MRR by 337%, and led demand generation at cnvrg.io, which Intel acquired in 2020. With 10+ years operating across every channel, motion, and stage, he hands the growth engine back once a team can run it without him.
- Elementor $200K→$20M ARR
- Riverside +337% Net MRR
- cnvrg.io → Intel
- 10+ yrs operating