Pulling structured entities (people, places, orgs, dates) from text.
1 min readupdated 2026-07-04
/ quick answer
Entity extraction converts unstructured text into typed fields. Modern approaches use an LLM with JSON mode and a schema instead of trained NER models. Pulling structured entities (people, places, orgs, dates) from text.
Pulling structured entities (people, places, orgs, dates) from text. Entity extraction converts unstructured text into typed fields. Modern approaches use an LLM with JSON mode and a schema instead of trained NER models. In practice: From 'Acme signed with Notion on March 3', extract { org_a: 'Acme', org_b: 'Notion', date: '2026-03-03' }. This dictionary node is part of the Onexial knowledge graph and links to related concepts, workflows and tools below.
Definition
Entity extraction converts unstructured text into typed fields. Modern approaches use an LLM with JSON mode and a schema instead of trained NER models.
Example
From 'Acme signed with Notion on March 3', extract { org_a: 'Acme', org_b: 'Notion', date: '2026-03-03' }.
Entity extraction converts unstructured text into typed fields. Modern approaches use an LLM with JSON mode and a schema instead of trained NER models.
What is an example of Entity Extraction?
From 'Acme signed with Notion on March 3', extract { org_a: 'Acme', org_b: 'Notion', date: '2026-03-03' }.
Why does Entity Extraction matter for AI and automation?
Pulling structured entities (people, places, orgs, dates) from text. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.