Structured data about people
The schema.org vocabulary for a person and the page about them, in JSON-LD.
- Person schema: every schema.org Person property for a professional, with a full JSON-LD example.
- sameAs: how sameAs links one person across sites, best practices and common mistakes.
- @id and stable identifiers: why a person needs a stable identifier, and the page versus entity (#person) pattern.
- ProfilePage: the schema.org type for profile pages, and how SelfBadge uses it.
- Person markup generator: a tool that writes schema.org Person JSON-LD for your own site.
Identity and disambiguation
How a person is identified across sites and told apart from people with the same name.
- Disambiguation: how machines tell people with the same name apart.
- Person identifiers compared: Wikidata QID, ORCID iD, ISNI, VIAF, Library of Congress, Crunchbase and others.
- Entity resolution: how knowledge graphs merge records about the same person.
- How AI assistants identify people: the signals assistants use, and why namesakes get mixed up.
Data sources and history
Where structured knowledge about people comes from, and how earlier efforts organized the web.
- Open data sources about people: Wikidata, DBpedia, OpenAlex and ORCID public data, and their licenses.
- Web directories: DMOZ to Curlie: the Open Directory Project, its successor, and what they meant for structured knowledge.
SelfBadge for machines
How SelfBadge verifies facts and how bots, agents and the AI check use them.
- Verification levels: how SelfBadge decides what is verified, method by method.
- Reading a SelfBadge profile: for bots and agents: JSON-LD, .json, .md, llms.txt, the API and the MCP server.
- The AI check method: how the AI check asks each engine, reads its sources and analyzes the answers.
For developers
- Developers: the lookup API and the MCP server, limits and keys.
- llms.txt and llms-full.txt: SelfBadge described for language models.