Named Entity Recognition

Named entity recognition (NER) is the process by which AI systems identify and classify specific references to people, organizations, products, locations, and other named things within text. In GEO, NER is the mechanism that connects your content to your brand: if the AI cannot recognize your brand name as a named entity in your content, it cannot attribute the content to you or cite you by name.

How NER Affects AI Citation

AI retrieval systems use NER to map content to entities in the knowledge graph. An analysis of 1.2 million ChatGPT citations found that cited content averages 20.6% entity density, compared to 5 to 8% in typical English text. Content with high entity density gives the AI more named references to anchor its understanding. When your content names specific people, tools, companies, and data sources, NER identifies each one and creates retrieval pathways that generic, entity-free content cannot match.

Writing NER-Friendly Content

Use full brand names, not pronouns. “Citate measures AI citation rates across ChatGPT and Perplexity” is recognizable by NER. “We measure citation rates across platforms” is not. The AI cannot attribute the second sentence to any entity.
Name specific people and sources. “Dan Petrovic at DEJAN found that grounding budgets average 2,000 words” contains three named entities. “Research found that budgets average 2,000 words” contains zero.
De-reference extracted passages. When an AI extracts a passage from your page, all surrounding context is stripped. If the passage says “their platform” instead of “Citate’s platform,” NER cannot identify the entity. Every passage must be self-contained with explicit entity names.
Include numbers as entities. Specific figures like “38% of AI Overview citations” and “20.6% entity density” function as named references that NER treats as retrievable data points.

For the complete content optimization framework, see the Generative Engine Optimization guide.