Search engines increasingly evaluate content as connected entities rather than keyword strings. Entity disambiguation is the technical process of telling a search engine precisely which person, place, organisation, or concept your content refers to, rather than leaving it to infer.
Ambiguous vs. disambiguated schema
A plain author string and topic string leave everything to inference:
{
"@type": "Article",
"author": "Paul Lovell",
"about": "SEO"
}Linking those same nodes to authoritative external identifiers removes the ambiguity:
{
"@context": "https://schema.org",
"@graph": [
{
"@type": "Article",
"headline": "Entity Disambiguation in Technical SEO",
"author": {
"@type": "Person",
"@id": "https://www.paullovell.uk/#paul-lovell",
"name": "Paul Lovell",
"sameAs": [
"https://www.linkedin.com/in/paul-lovell/",
"https://www.semrush.com/blog/user/155185249/"
]
},
"about": [
{
"@type": "Thing",
"name": "Search Engine Optimization",
"sameAs": "https://en.wikipedia.org/wiki/Search_engine_optimization"
}
]
}
]
}Building an entity graph that holds together
The value compounds when the same identifiers are used consistently: the same sameAs array on the Person schema, referenced by @id from every article's author field, cross-linked with knowsAbout entries pointing at Wikipedia or Wikidata concepts. Inconsistent identifiers across pages dilute exactly the signal this is meant to build.
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Written by
Paul Lovell
International SEO Consultant & Founder of Always Evolving SEO. 15+ years running technical audits, log-file analysis, and root-cause fixes for multimillion-dollar businesses — speaker at SMX London, Search & Content Summit, and SEMrush events.