How to Reduce LLM Hallucinations with Wikidata: Hands-On Fact-Checking Using MCP
LLMs are powerful, but they still hallucinate facts, especially when asked about entities, relationships, or claims that require up-to-date or structured knowledge.
In this hands-on workshop, we'll explore how to use Wikidata as a grounding and fact-checking layer for LLMs to reduce hallucinations and make AI systems more reliable.
We'll start with a short introduction to Wikidata and then set up the Wikidata MCP so an LLM can retrieve and verify facts rather than relying solely on its internal memory. This already provides a practical way to ground LLM outputs in verifiable data.
From there, we’ll go beyond LLM-only approaches and build a small experimental fact-checking pipeline. The system combines semantic retrieval, LLM-based reranking, and natural language inference (NLI) to validate claims against evidence in a more controlled and interpretable way.
This workshop focuses on evidence-driven verification pipelines that make LLM's reasoning steps explicit and easier to inspect, debug, and improve.
What we'll cover
Wikidata as a structured source for factual verification
Setting up and querying Wikidata using MCP
Verifying claims with MCP + an LLM
Moving beyond pure GenAI to evidence-based fact-checking
Finding relevant Wikidata statements with semantic search
Ranking candidate evidence with an LLM
Verifying claims using an NLI model
What you'll leave with
By the end of the workshop, you'll be able to:
Ground LLM outputs in structured data to reduce hallucinations
Understand when LLM-only fact-checking is not enough
Build a small, transparent fact-checking pipeline you can adapt to real projects
About the speaker:
Philippe Saadé is the AI/ML project manager at Wikimedia Deutschland. His current work focuses on making Wikidata accessible to AI applications with projects like the Wikidata vector database and the Wikidata Model Context Protocol.
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This event is sponsored by Wikimedia