Prismix
arXiv — NLP / Computation & Language · · 3 min read

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

#version-bump #paper

Mirrored from arXiv — NLP / Computation & Language for archival readability. Support the source by reading on the original site.

Like Read original ↗

Computer Science > Computation and Language

arXiv:2507.05740 (cs)
[Submitted on 8 Jul 2025 (v1), last revised 1 Jul 2026 (this version, v2)]

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

View a PDF of the paper titled GPTKB v1.5: A Massive Knowledge Base for Exploring Factual LLM Knowledge, by Yujia Hu and 4 other authors
View PDF HTML (experimental)
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.
Comments: 3 pages, 1 figure, 1 table
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2507.05740 [cs.CL]
  (or arXiv:2507.05740v2 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2507.05740
arXiv-issued DOI via DataCite
Journal reference: AAAI 2026: Demo track
Related DOI: https://doi.org/10.1609/aaai.v40i48.42354
DOI(s) linking to related resources

Submission history

From: Tuan-Phong Nguyen [view email]
[v1] Tue, 8 Jul 2025 07:37:12 UTC (574 KB)
[v2] Wed, 1 Jul 2026 09:48:35 UTC (53 KB)
Full-text links:

Access Paper:

Current browse context:

cs.CL
< prev   |   next >
Change to browse by:
cs

References & Citations

Loading...

BibTeX formatted citation

loading...
Data provided by:

Bookmark

BibSonomy Reddit
Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos

Demos

Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers

Recommenders and Search Tools

Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Discussion (0)

Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.

Sign in →

No comments yet. Sign in and be the first to say something.

More from arXiv — NLP / Computation & Language