Spatio-temporal logical analysis refers to accurately expressing spatio-temporal relationships between entities using logical symbols. Traditional spatio-temporal logical analysis adopts two paradigms: closed-domain and open-domain approaches. Closed-domain methods predefine symbolic systems for representing spatio-temporal logic and then translate natural language into logical expressions. While ensuring accurate representation of spatio-temporal relationships, such methods face limitations in handling complex relationships due to the constraints of artificial definitions. Open-domain approaches extract keywords to represent spatio-temporal relationships using natural language itself. Although capable of covering complex relationships, these methods suffer from the semantic ambiguity inherent in natural language, resulting in imprecise logical representations. The purpose of this study is to convert natural language expressions of spatio-temporal relationships into logical language, enabling more precise representation of spatio-temporal information. To address the forementioned issues, this study considered the linguistic observation that spatio-temporal relationships in language are primarily expressed through localizers. By defining the semantics of localizers through logical symbols, the proposed framework aims to overcome both the insufficiency of coverage and the lack of precision. Accordingly, a spatio-temporal logical framework for localizers is established, including 1) the design of annotation specifications that define the logical expression scope of localizers and provide detailed annotation guidelines; 2) manual annotation of 6190 samples from the People’s Daily and CTB datasets to construct a task-specific corpus based on the proposed specifications; 3) application of large language models to perform logical reasoning on localizer-triggered spatio-temporal expressions, achieving an accuracy exceeding 70% based on corpus-driven inference.