Automatic fake news detection in Portuguese is still often treated as a text classification task, without explicitly representing the factual veracity of the claims. In this work, we evaluate LLM-based triple extraction in a knowledge graph (KG)-based fact-checking system. We replace the Open Information Extraction component of a previous approach with triples generated by Sabiá 4, while keeping the remaining pipeline unchanged. The graph is built only from triples extracted from true news articles and is used as factual support to verify new instances. The experiments use true and fake news articles across four evaluation settings. In the closed setting with the complete KG, LLM-based extraction achieves an F1 score of 0.9992, outperforming the OIE-based configuration. However, in more restrictive evaluation settings, expressive triples require entity normalization, relation standardization, and semantic consolidation to provide factual support beyond direct evidence.

The proliferation of fake news in digital environments poses serious challenges to democratic processes, particularly in morphologically rich languages such as Portuguese. While most existing approaches focus on stylistic cues or propagation patterns in social networks, this paper proposes an automated fake news verification methodology grounded in Knowledge Graphs (KGs). Instead of treating news as raw text, we represent each article as a set of factual events encoded as semantic triples of subject, predicate, and object. A proprietary knowledge graph is built from Brazilian data sources, and a verification algorithm is introduced to estimate the veracity of news articles based on graph connectivity evidence. Experimental results confirm the feasibility of the proposed approach and highlight its inherent explainability as a key advantage over deep learning black-box models. Error analysis further indicates that the main limitation stems from the syntactic complexity of Open Information Extraction in Portuguese, suggesting that improvements at this extraction stage are essential to increase system robustness.