Language models are powerful tools, 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 (Hu et al., ACL 2025). The demonstration experience 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 research area of systematic analysis of LLM knowledge, as well as for automated KB construction. The GPTKB demonstrator is accessible at https://gptkb.org.
@article{arxiv.2507.05740,
title = {GPTKB v1.5: A Massive Knowledge Base for Exploring Factual LLM Knowledge},
author = {Yujia Hu and Tuan-Phong Nguyen and Shrestha Ghosh and Moritz Müller and Simon Razniewski},
journal= {arXiv preprint arXiv:2507.05740},
year = {2025}
}