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

arXiv: https://arxiv.org/abs/2507.05740 · HTML: https://mlsanity.com/paper/2507.05740/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 18 references that are in the map.
- First posted on arXiv 8 Jul 2025.

## Builds on (most cited first, 11 of 11)

- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [How Can We Know What Language Models Know?](https://mlsanity.com/paper/1911.12543/) (2019)
- [How Much Knowledge Can You Pack Into the Parameters of a Language Model?](https://mlsanity.com/paper/2002.08910/) (2020)
- [CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data](https://mlsanity.com/paper/1911.00359/) (2019)
- [The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A"](https://mlsanity.com/paper/2309.12288/) (2023)
- [Head-to-Tail: How Knowledgeable are Large Language Models (LLMs)? A.K.A. Will LLMs Replace Knowledge Graphs?](https://mlsanity.com/paper/2308.10168/) (2023)
- [Crawling the Internal Knowledge-Base of Language Models](https://mlsanity.com/paper/2301.12810/) (2023)
- [Can Generative Pre-trained Language Models Serve as Knowledge Bases for Closed-book QA?](https://mlsanity.com/paper/2106.01561/) (2021)
- [Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases](https://mlsanity.com/paper/2009.11564/) (2020)
- [Enabling LLM Knowledge Analysis via Extensive Materialization](https://mlsanity.com/paper/2411.04920/) (2024)
- [Evaluating the Knowledge Base Completion Potential of GPT](https://mlsanity.com/paper/2310.14771/) (2023)

## Built on by (most cited first, 5 of 5)

- [Foundations of LLM Knowledge Materialization: Termination, Reproducibility, Robustness](https://mlsanity.com/paper/2510.06780/) (2025)
- [Auditing a KB Elicitation of Frontier LLM Knowledge: A Multi-dimensional Analysis of GPTKB v1.5](https://mlsanity.com/paper/2510.07024/) (2025)
- [LLMpedia: A Transparent Framework to Materialize an LLM's Encyclopedic Knowledge at Scale](https://mlsanity.com/paper/2603.24080/) (2026)
- [Beyond Questions: Evaluating the Stated Knowledge of LLMs](https://mlsanity.com/paper/2605.26937/) (2026)
- [GPTKB 2.0: Browsing, Querying, and Auditing a Disambiguated LLM-Derived Knowledge Base](https://mlsanity.com/paper/2608.06992/) (2026)

## Abstract (arXiv)

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.