Qualified Cross-References as a Verification Method: The Normative Environment of the EU AI Act

Legal cross-references are commonly represented as links between instruments or provisions. For a curated legal knowledge base, the existence of a link is only the beginning of the claim: it must also state the legal character of the interaction, identify the provisions supporting it, preserve its conditions, and remain consistent when reached from either instrument. This paper presents a provision-level model and a construction protocol for qualified cross-references, developed through a bilingual corpus of fourteen instruments surrounding Regulation (EU) 2024/1689 (the AI Act). The model distinguishes direct textual reference, bounded presumption of conformity, substantive interaction without textual reference, mediated intersection, and institutional analogy, and treats applicative interaction and definitional overlap as independent dimensions. The methodological contribution is bidirectional inversion: a relationship documented from act A towards act B is reconstructed from B's perspective against the provisions of both. Inversion is not a duplicate table but a verification operation that tests provisions, qualification, direction, and conditions before deciding how the relationship should be rendered from either side. Applied during construction, the protocol surfaced six incorrect article references, three inaccurate legal qualifications, and one divergence between two published descriptions of the same interaction. The corpus also shows why qualification matters: one reference to Regulation (EU) 2019/881 carries the AI Act's bounded cybersecurity presumption for high-risk systems, while related product legislation uses the same certification framework through legally distinct mechanisms. The contribution is thus a map of one regulatory environment and a reproducible method for making curated cross-reference knowledge bases inspectable and internally testable.