Harnessing LLM Ensembles for KG-Grounded Narrative Extraction: Disinformation vs. Trustworthy News

Due to the rapid spread of disinformation, it is becoming increasingly difficult for the public to understand current events and how discussions and decisions are made in democratic societies. We propose a KG-grounded narrative extraction pipeline to compare disinformation and trustworthy news. English articles (2015–2023), included in EUvsDisinfo cases and matched mainstream coverage, were converted to AMR-based RDF graphs, and LLM ensembles were used to extract characters, events, causal links and framing edges grounded in these graphs. We studied two ensemble policies: a recall-oriented union that retained all model outputs and a precision-oriented consensus that kept only agreed elements, plus an LLM critic that flagged missing links, contradictions and framing inconsistencies. On an expert-annotated subset of 60 articles, the extractor ensemble attained very high precision for characters (0.99) and events (0.97) and solid performance for causal links (0.77) and framing edges (0.84), with similar scores for both classes. Our critic ensemble reached 0.74 precision. Structurally, union and consensus operated over the same grounded nodes but differed significantly in relational density, thus achieving rich vs. skeletal narrative graphs. Linking our narratives to GDELT showed that ≈97% of extracted actors and events appeared in global news for both classes, while directional actor pairs from causal links were less often supported for disinformation (0.45) than trustworthy news (0.60). Overall, disinformation and trustworthy articles shared event backbones but diverged in the density and (to a lesser extent) directionality of causal attributions and framing relations.