{
  "@context": "https://schema.org",
  "@type": "article",
  "@id": "https://anchorfact.org/kb/information-extraction",
  "headline": "Information Extraction: NER, Relation Extraction, and LLM-Powered IE",
  "description": "Information Extraction (IE) transforms unstructured text into structured knowledge. Named Entity Recognition identifies people, organizations, and locations; Relation Extraction discovers connections between them. Modern LLMs have fundamentally changed IE — from specialized models to unified generative approaches.",
  "dateCreated": "2026-05-24T02:49:13.619Z",
  "dateModified": "2026-05-24",
  "author": {
    "@type": "Organization",
    "name": "AnchorFact"
  },
  "publisher": {
    "@type": "Organization",
    "name": "AnchorFact",
    "url": "https://anchorfact.org"
  },
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "anchorfact:confidence": "high",
  "anchorfact:generationMethod": "ai_assisted",
  "citation": [
    {
      "@type": "CreativeWork",
      "name": "Structured information extraction from scientific text with large language models",
      "sameAs": "https://www.nature.com/articles/s41467-024-45563-x"
    },
    {
      "@type": "CreativeWork",
      "name": "Large Language Models for Generative Information Extraction: A Survey",
      "sameAs": "https://arxiv.org/abs/2312.17616"
    }
  ]
}