GPTKB v1.5: A Massive Knowledge Base for Exploring Factual LLM Knowledge

Proceedings of the AAAI Conference on Artificial Intelligence 2026 Open access

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Authors: Yujia Hu, Tuan-Phong Nguyen, Shrestha Ghosh, Moritz Müller, Simon Razniewski
DOI: 10.1609/aaai.v40i48.42354
Cited by: 0

Abstract

Language models are powerful artifacts, yet their factual knowledge is still poorly understood, and inaccessible to ad-hoc browsing and scalable statistical analysis. This demonstration introduces GPTKB v1.5, a densely interlinked 100-million-triple knowledge base (KB) built for $14,000 from GPT-4.1, using the GPTKB methodology for massive-recursive LLM knowledge materialization. This demo focuses on three use cases: (1) link-traversal-based LLM knowledge exploration, (2) SPARQL-based structured LLM knowledge querying, (3) comparative exploration of the strengths and weaknesses of LLM knowledge. Massive-recursive LLM knowledge materialization is a groundbreaking opportunity both for the systematic analysis of LLM knowledge, as well as for automated KB construction.

Fields of Study & Subjects

Natural Language Processing Techniques Topic Modeling Text Readability and Simplification

Publication details

Journal / source
Proceedings of the AAAI Conference on Artificial Intelligence
Volume
40
Issue
48
Pages
41604–41606
Publication date
14 March 2026
Language
EN