Fact-checking assesses what available evidence supports, but an unresolved verdict does not determine how to address the concern an utterance expresses. We propose an NLP representation that records factual content, evidence status, and communicative functions, such as expressing distrust or demanding accountability, to inform human-reviewed responses. We reanalyze two selected Korean annotation sets containing 500 COVID-19 claims and 150 hantavirus claims. Insufficient verdicts account for 84.8% and 76.7%, respectively, reflecting source categories related to utterance form or limited evidence. Factual content and communicative function are treated as overlapping dimensions, allowing verification to accompany explanation, accountability, or empathy. In an exploratory three-rater follow-up on 200 stratified items, majority judgments identify both dimensions in 107 items (53.5%), including 65/110 items classified as structurally insufficient. Agreement varies across functions (𝛼=0.505–0.881), indicating that their boundaries require refinement. A sensitivity analysis examines the source grouping, and six source-linked examples on severe fever with thrombocytopenia syndrome illustrate combined responses. These findings support representing both what an utterance claims and what it communicates, while retaining uncertainty about each. The study provides a design proposal and descriptive evidence; broader validation of the function categories, response policies, and downstream effects remains necessary.