Representing the temporal evolution of legal norms is a critical challenge for automated processing. While foundational frameworks exist, they lack a formal pattern for granular, component-level versioning, hindering the deterministic point-in-time reconstruction of legal texts required by reliable AI applications. This paper proposes a structured, temporal modeling pattern grounded in the LRMoo ontology. Our approach models a norm's evolution as a diachronic chain of versioned F1 Works, distinguishing between language-agnostic Temporal Versions (TV), each being a distinct Work, and their monolingual Language Versions (LV), modeled as F2 Expressions. The legislative amendment process is formalized through event-centric modeling, allowing changes to be traced precisely. Using the Brazilian Constitution as a case study, we demonstrate that our architecture enables the exact reconstruction of any part of a legal text as it existed on a specific date. This provides a verifiable semantic backbone for legal knowledge graphs, offering a deterministic foundation for trustworthy legal AI.

翻译：法律规范的时间演化表征是自动化处理中的关键挑战。尽管存在基础框架，但这些框架缺乏对细粒度组件级版本管理的正式模式，阻碍了可靠AI应用所需的确定性时间点法律文本重构。本文提出一种基于LRMoo本体的结构化时间建模模式。该方法将法律规范的演化建模为带版本F1作品的历时链，区分语言无关的时间版本（每个均视为独立作品）及其单语语言版本（建模为F2表达）。立法修正过程通过事件驱动建模实现形式化，使得变更追踪具有精确性。以巴西宪法为案例，我们证明该架构能够精确重建特定日期存在的法律文本任意部分。这为法律知识图谱提供了可验证的语义骨架，为可信赖的法律AI奠定确定性基础。