---
title: "Discussion on \"Committing a Fact: A Trust Boundary for LLM-Extracted Graph Data\" | Hashnode"
description: "Discussion on \"Committing a Fact: A Trust Boundary for LLM-Extracted Graph Data\". A language model reads a sentence from a news wire and returns, cleanly formatted, {value: 40, property: casualty_count, source: \"Source X\", confidence: \"high\"}. It looks like a fact. It is a guess: a"
url: "https://hashnode.com/posts/committing-a-fact-a-trust-boundary-for-llm-extracted-graph-data/6a20edfabaa33b25bdc7d746"
---

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# [Committing a Fact: A Trust Boundary for LLM-Extracted Graph Data](https://muhanadabulhusn.hashnode.dev/committing-a-fact-a-trust-boundary-for-llm-extracted-graph-data?utm_source=hashnode&utm_medium=feed)

A language model reads a sentence from a news wire and returns, cleanly formatted, {value: 40, property: casualty\_count, source: "Source X", confidence: "high"}. It looks like a fact. It is a guess: a

Jun 4 · 11 min read · muhanadabulhusn.hashnode.dev  
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## Written by

[Muhanad Abulhusn](https://hashnode.com/@muhanadabulhusn)

@muhanadabulhusn · Amsterdam, Netherlands

  
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Reproducible social data science — and the tools that make it reproducible.

I'm a social data analyst working at the intersection of data science, AI integration, and sociopolitical research. I build reproducible studies — open data, validated methods, robustness checks, honest limitations — on questions like how the press frames African elections, where internet shutdowns cost the most, and how violence and displacement move through a conflict. I also build the open-source tooling that makes that kind of analysis repeatable: graph-investigation workbenches and retrieval systems for reading social dynamics out of text. Based in the Netherlands. Python-first, provider-agnostic by design. That's the full-strength version. It opens with the identity, names>

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