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GPTKB 2.0: Browsing, Querying, and Auditing a Disambiguated LLM-Derived Knowledge Base

arXiv 2608.06992 ↗First posted 7 Aug 2026cs.CL cs.AI cs.DBcited by 0

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Abstract (arXiv)

We present a web demo for exploring a large-scale disambiguated knowledge base (KB) materialized from a large language model (LLM). GPTKB 2.0 contains 38.4M triples over 1.6M canonical entities, together with 207.6K consolidated relations and 66K consolidated classes. Unlike prior LLM-derived knowledge bases that largely identify entities by surface strings, GPTKB 2.0 performs context-guided disambiguation during recursive KB construction, separating homonyms and merging synonymous mentions as facts are elicited. The demo makes this process inspectable: users can browse entities, follow links across the KB, and audit the provenance of individual facts, including surface forms, candidate matches, source triples, and disambiguation decisions. The interface further supports structured SPARQL queries, natural-language questions translated to SPARQL, and entity linking from user-provided text to canonical GPTKB 2.0 entries. GPTKB 2.0 is available at https://gptkb.org/, with the full KB downloadable for offline use.

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References · 9

  1. 2019Language Models as Knowledge Bases?cited by 1,931
  2. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,268
  3. 2024VERISCORE: Evaluating the factuality of verifiable claims in long-form text generationcited by 116
  4. 2023Crawling the Internal Knowledge-Base of Language Modelscited by 85
  5. 2024Cultural Commonsense Knowledge for Intercultural Dialoguescited by 20
  6. 2024Enabling LLM Knowledge Analysis via Extensive Materializationcited by 12
  7. 2026LELA: an LLM-based Entity Linking Approach with Zero-Shot Domain Adaptationcited by 6
  8. 2025GPTKB v1.5: A Massive Knowledge Base for Exploring Factual LLM Knowledgecited by 5
  9. 2026LELA: An End-to-end LLM-based Entity Linking Framework with Zero-shot Domain Adaptationcited by 1

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