Named Entity Recognition
NLPAn NLP task that identifies and classifies named entities such as people, organizations, locations, and dates in unstructured text.
Named Entity Recognition (NER) is a fundamental natural language processing task that automatically identifies and classifies mentions of named entities in text into predefined categories such as Person, Organization, Location, Date, Product, and others. For example, in the sentence "Alice Smith works at Acme Corp in San Francisco," NER identifies "Alice Smith" as a Person, "Acme Corp" as an Organization, and "San Francisco" as a Location.
NER is a critical component of knowledge graph construction pipelines. Before you can build a graph of entities and relationships, you must first identify which entities exist in your text. Common NER tools include spaCy (fast, rule-based plus statistical), Hugging Face transformer models (higher accuracy), and LLM-based extraction (most flexible but slower and more expensive).
In RAG+KG systems, NER serves multiple roles: extracting entities from source documents during knowledge graph construction, identifying entities in user queries for entity-centric retrieval, and supporting entity-aware chunking that preserves named entities within chunk boundaries.
Last updated: February 22, 2026