# From Articles to Premises: Building PrimeFacts, an Extraction Methodology and Resource for Fact-Checking Evidence Source: [From Articles to Premises: Building PrimeFacts, an Extraction Methodology and Resource for Fact-Checking Evidence]() Collection index: [llms.txt]() Data updated: 2026-07-21T05:00:05.525Z Authors: Premtim Sahitaj, Jawan Kolanowski, Ariana Sahitaj, Veronika Solopova, Max Upravitelev, Daniel R\\"oder, Iffat Maab, Junichi Yamagishi, Sebastian M\\"oller, Vera Schmitt Published: 2026-05-07T10:58:29.000Z ## Abstract Fact-checking articles encode rich supporting evidence and reasoning, yet this evidence remains largely inaccessible to automated verification systems due to unstructured presentation. We introduce PrimeFacts, a methodology and resource for extracting fine-grained evidence from full fact-checking articles. We compile 13,106 PolitiFact articles with claims, verdicts, and all referenced sources, and we identify 49,718 in-article hyperlinks as natural anchors to pinpoint key evidence. Our framework leverages large language models (LLMs) to rewrite these anchor sentences into stand-alone, context-independent premises and investigates the extraction of additional implicit evidence. In evaluations on cross-article evidence retrieval and claim verification, the extracted premises substantially improve performance. Decontextualized evidence yields higher retrievability, achieving up to a 30 percent relative gain in Mean Reciprocal Rank over verbatim sentences, and using the evidence for verdict prediction raises Macro-F1 by 10-20 points over the baseline. These gains are consistent across different verdict granularities (2-class vs. 5-class) and model architectures. A qualitative analysis indicates that the decontextualized premises remain faithful to the original sources. Our work highlights the promise of reusing fact-checkers' evidence for automation and provides a large-scale resource of structured evidence from real-world fact-checks. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Claim Verification on PrimeFacts Two Class](): Claim Verification; PrimeFacts Two Class; Macro F1: 81. - [Claim Verification on PrimeFacts Five Class](): Claim Verification; PrimeFacts Five Class; Macro F1 Score: 42. ## Original sources and implementation [Original paper]() [PDF]()