The democratization of LLMs has accelerated the generation and circulation of highly fluent disinformation, making traditional syntax-semantic verification increasingly insufficient. Such deception often combines persuasive rhetoric, emotional manipulation, and narrative role construction to shape readers’ interpretations. However, existing detectors typically emphasize isolated signals—such as syntax, external knowledge, persuasion, or affective cues—and thus struggle to capture multi-faceted manipulation or provide structured, taxonomy-grounded evidence. To address this gap, we present ExTax, a taxonomy-aligned framework for interpretable disinformation detection. ExTax unifies persuasive rhetoric, emotional manipulation, and narrative roles into a 17-dimensional taxonomic space comprising 6 persuasive-rhetoric strategies, 5 emotional-manipulation methods, and 6 narrative-role categories. It elicits attributes from multiple frontier LLMs, reconciles their disagreements through Entropy-driven Dynamic Label Smoothing, and fuses the resulting taxonomic representations with contextual encodings via Heterogeneous Multi-Head Attention. Across five cross-domain and cross-genre benchmarks, ExTax achieves an overall Macro F₁ of 0.8456, outperforming strong deep learning and LLM-based baselines. It also remains robust under severe genre imbalance, where the strongest deep baseline drops from 0.9454 to 0.6194 across genres. Our code is available at https://github.com/owlsan49/ExTax.