named entity recognition
Also called: NER
Named entity recognition (NER) is a natural language processing task that detects named things in text (people, places, organizations, dates, products) and classifies each into a type. Search engines and AI models use it to turn raw text into structured entities they can link, disambiguate, and reason about.
From strings to entities
NER runs in two moves. Detection finds the span of text that names something (“Barcelona”, “Tim Cook”, “the iPhone 15”). Classification tags each span with a type: PERSON, LOCATION, ORGANIZATION, DATE, and so on. Google’s Natural Language API returns those types, and where it can, it attaches a mid (a Knowledge Graph ID) plus a wikipedia_url so the mention resolves to one specific real-world thing instead of a bare string of characters.
That resolution step, called entity linking, matters more than the tagging. “Jordan” could be a person, a country, or a shoe brand. Linking picks the right one by matching the mention to a canonical ID (a Wikidata Q-number or a Google MID) and folding synonyms and spelling variants into it. Once your page is decoded into linked entities, a search engine can score it against its knowledge graph for relevance, authority, and trust, rather than matching keywords letter for letter.
The models are statistical, so how well you get recognized depends on how clearly you name things. A page that opens a passage with a clear canonical name, states verifiable facts, and stays consistent across titles, headings, and alt text is far easier for a machine to disambiguate than one that leans on pronouns and vague references.
How it affects your traffic
AI answer engines run this exact pipeline before they cite anyone. They pull the entities out of your pages, link each one to a canonical ID, and favor sources whose entities are clean, consistent, and verifiable. If your brand, products, and authors are not recognizable as distinct entities, you stay invisible to the citation layer no matter how well the prose reads. Our AI SEO work builds that entity foundation (schema with sameAs links, consistent naming, entity hubs) so your pages get recognized and cited in AI search.
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