The first formal artifact of the project is the ontological taxonomy: a seven-dimensional framework for classifying AI-generated claims before any truth evaluation takes place.
Dimension D1 — Content Type is the load-bearing axis. It currently includes eleven active categories (D1.1 through D1.10, plus D1.14), covering the full range from historical and statistical claims to normative, predictive, and metaphysical ones. The remaining dimensions — D2 (temporality), D3 (epistemic certainty), D4 (causal structure), D5 (framework-dependency), D6 (verifiability mode), D7 (falsifiability) — operate as modifiers that describe how a given claim type behaves epistemically.
The governing principle, which became the paper’s title, is this: ontology precedes epistemology. You cannot determine whether a claim is true without first establishing what type of claim you are dealing with. A statistical claim and a normative claim share nothing in their truth conditions — treating them as interchangeable is the error the framework is designed to prevent.
Version 2.0 established the core structure. Two refinements followed in the same month.
Current version: 2.2 — February 2026. Document: tassonomia_completa_v2_2.docx