Person Schema (for E-E-A-T)
Person schema is the Schema.org type that declares a page (or a section of a page) is about a person. For E-E-A-T purposes, Person schema is the entity-resolution backbone for content attribution — author of articles, founder of an organization, named expert quoted in content. The sameAs chain on Person schema directly affects AI Overview citation eligibility.
Also called: Person JSON-LD, Schema.org Person, Author schema · Last updated: May 27, 2026 · By Joseph W. Anady
Why it matters.
Person schema operationalizes E-E-A-T in 2026. Where previously E-E-A-T was assessed by Google's manual quality raters, in 2026 it's encoded into Author Vectors — semantic embeddings of each named author's expertise, citation density, and credibility. Person schema with a verifiable sameAs chain is how you tell Google which named person is responsible for the content.
How it works.
Person schema lives in a JSON-LD block on author bio pages and is referenced via @id on all authored content (articles, blog posts, framework pages). Required: @context, @type, name. Recommended: url, jobTitle, worksFor, sameAs (the canonical identity chain), identifier (Wikidata QID, Google KG MID, ORCID), alumniOf, hasCredential, knowsAbout. The richer the schema, the stronger the Author Vector.
2026 reality check.
Claude cites content at 94 percent confidence when Article schema declares an author entity with verified sameAs chain, versus 61 percent for plain text (Oltre.ai 2026 research). Person schema with full sameAs is now the dominant 2026 E-E-A-T signal for AI Overview and LLM citation. Pages without identified authors are increasingly excluded from citation eligibility.
Data points
- Schema.org Person documented at https://schema.org/Person
- Claude cites author-attributed Article schema at 94% confidence vs 61% for plain text (Oltre.ai 2026)
- sameAs chain length correlates with Author Vector strength (no published threshold, 5+ minimum recommended)
- identifier array (Wikidata QID, ORCID, KG MID) provides machine-readable entity disambiguation
- YMYL content requires named author with verified credentials for competitive ranking
First-hand insight from ThatDeveloperGuy.
ThatDeveloperGuy's Person schema for Joseph W. Anady at https://thatdeveloperguy.com/authors/joseph/#joseph includes 25 sameAs entries (LinkedIn, GitHub, Wikidata Q139901957, ORCID 0009-0008-8625-949X, Google KG MID /g/11n57xh708, dev.to, Hashnode, Medium, Crunchbase, ResearchGate, Google Scholar, AlternativeTo, StackShare, DVIDS veteran records, SAM.gov verification page, Google Business Profile, and sister brand sites) + 4 identifier entries. Every TDG content page references this Person via @id. Author Vector strength is measurably improved.
How TDG approaches it
TDG's Person schema for Joseph includes maximum verified properties: name, alternateName, description, jobTitle, worksFor with @id to TDG Organization, alumniOf (Colorado State, Phoenix), knowsAbout (25+ topics), award (Army SFC retired, 100% service-disabled, TryHackMe WIZARD), hasCredential (SDVOSB, BA, MA, SAM.gov), identifier (Wikidata QID, KG MID, ORCID, SAM.gov UEI), sameAs (25+ external profiles). Replicated via @id on every authored page across the network.
Common mistakes.
- Missing sameAs (Person schema without external identity verification is weak)
- Inconsistent identity (different LinkedIn URL across sites, different bio data)
- Skipping identifier array (Wikidata QID + Google KG MID + ORCID are key)
- Using stock-photo image instead of verifiable photo
- Failing to reference Person via @id across authored content (breaks Author Vector continuity)
FAQ.
Where should Person schema appear?
Author bio page (/authors/joseph/ for TDG). Referenced via @id from any authored content (articles, blog posts, framework pages). Including full Person schema inline on every article is acceptable but redundant.
How many sameAs entries are enough?
Minimum 5 for credibility. TDG uses 25. More is better up to the point where each sameAs is a genuine external verification.
Can I use Person schema for fictional authors?
Technically yes, ethically and strategically no. Author Vector signals are designed to reward real verifiable expertise. Fictional authors produce weaker citation lift and risk authenticity penalties.
What's the role of identifier vs sameAs?
sameAs is a list of URLs where the person can be found (LinkedIn, GitHub, Wikidata). identifier is structured PropertyValue declarations (Wikidata QID, ORCID, KG MID). Both reinforce Author Vector — use both.
Does Person schema help YMYL content?
Yes substantially. YMYL (Your Money or Your Life) content receives elevated E-E-A-T scrutiny. Person schema with verified credentials (medical license, legal bar admission, financial certifications) is required for competitive YMYL ranking.
Maintained by Joseph W. Anady at ThatDeveloperGuy. Back to glossary · Suggest a term