GPTKB 2.0: Browsing, Querying, and Auditing a Disambiguated LLM-Derived Knowledge Base
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 m...
| Authors | Yujia Hu, Tuan-Phong Nguyen, Simon Razniewski |
|---|---|
| Primary category | cs.CL |
| Published | 7 August 2026 |
| Last updated at source | 2 September 2026 |
Models and datasets referencing this paper
- Knowledge-aware-AI/GPTKB_v2Dataset
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