What is Entity Extraction?
Entity extraction is the process of identifying and extracting structured information about specific entities (people, organizations, locations, dates, products, concepts, etc.) from unstructured text. This technique goes beyond simple named entity recognition to include extracting attributes, relationships, and contextual information about the identified entities, creating structured knowledge that can be stored in entity stores or knowledge graphs.
The extraction process typically involves multiple steps: detecting entity mentions in text, classifying them by type, resolving references (understanding when different mentions refer to the same entity), and extracting associated attributes and facts. Modern approaches often use LLMs to perform entity extraction through carefully designed prompts or fine-tuned models, which can identify complex entity types and extract nuanced information beyond what traditional NER systems handle.
In agent memory systems, entity extraction enables more sophisticated information tracking and retrieval. Rather than just remembering raw conversation text, agents can maintain structured knowledge about people, places, and things mentioned in interactions. This structured representation supports better question answering (e.g., "What did we discuss about Project Alpha?"), enables entity-centric retrieval, and provides a foundation for building knowledge graphs that capture relationships between entities.