Entity Extraction

The process of identifying and classifying named entities in text -- people, places, organizations, products, dates -- and structuring them as machine-readable data.

Also known as: named entity recognition, NER, entity recognition

Entity extraction (also called named entity recognition or NER) is the process of reading unstructured text and identifying the specific real-world entities it mentions: people, organizations, locations, products, dates, quantities, and other defined categories.

The output of entity extraction is structured data: a record for each identified entity, with the entity name, type, span location in the source text, and often additional attributes and relationships.

Entity extraction is a core step in building knowledge graphs and AI datasets from unstructured content. When you have a large corpus of web pages, documents, or articles and want to convert it into a structured entity dataset, entity extraction is how you surface the entities from the text.

For AI visibility, entity extraction matters because it is often how AI systems learn to associate a brand, product, or topic with its attributes. If your name, your organization, and your domain frequently appear together in well-structured contexts, entity models are more likely to accurately classify and connect you.