Predicate of Identity of the Nature of Things: A Deductive Approach...

The predicate of identity method presents a deductive approach to verification of structural correspondence between a description of an entity (D) and its genesis — the nature of the thing (G). Unlike existing probabilistic methods (NLI, FEVER, RAG) that compare text to text, the proposed method compares description to reality through a four-component tuple (E, V, M, Σ) describing any entity regardless of its nature. The method introduces an axiological scale of 11 anomaly types, proven by a completeness theorem: the space of discrepancies D × G is exhausted by eleven types, ordered by the degree of identity violation — from data substitution (I) to emergent insufficiency (XI). Five operation modes (auto, direct, inverse, emergent, full) provide adaptive verification under conditions of incomplete or excessive description. The architecture includes an LLM adapter that delegates only the parsing of free text into a structured tuple to the language model, while verification remains deterministic. LLM hallucinations during parsing are detected by the core as anomalies — the principle "LLM = parsing, core = truth." Validation was conducted at six levels: 1,000,000 synthetic cases, 8 real cases on regulatory documents (FNP), 1,000 cross-domain cases across 10 subject areas, 55 foundational cases (Darwin's theory, the Bible, modern science vs. nature of things), 30 targeted cases for activating rare types, and 5,000 LLM validation cases. Result: 11 out of 11 types activated, 0 new types, 0 crashes, accuracy/precision/recall = 100%. Performance — 133,731 cases/sec on CPU without GPU, which is 130 times faster than BERT-based NLI. The method is applicable to any entity — process, thing, or phenomenon of the physical world — from subatomic interactions to social systems. keywords: - predicate of identity - deductive verification - structural correspondence - NLP - LLM validation - emergent insufficiency - anomaly detection - completeness theorem - axiological scale - deterministic NLP - hallucination detection