Correction — 28 August 2026. The codes in this note are right; two of the category names used to illustrate them were not. “Statistical” and “predictive” are not D1 categories: empirical-quantitative claims are D1.2, and predictive ones are handled under D1.4. The illustrative list has been corrected in place; the eleven active codes it cites are unchanged and were correct when written. Current state: Project status.
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-factual and empirical-quantitative claims to ethical-normative and metaphysical-ontological 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.pdf
Code assigned retrospectively on 23 August 2026, when the notation was introduced: a declared reconstruction, not a real-time record.
The formal apparatus of this note was built in dialogue with a language model and verified line by line. The verification is the part that counts: no step was accepted on the strength of the output’s apparent authority.