This paper describes our system developed for SemEval-2025 Task 1, subtask A. This sharedsubtask focuses on multilingual idiom recognition and the ranking of images based on howwell they represent the sense in which a nominal compound is used within a given contextual sentence. This study explores the use of a pipeline, where task-specific models are sequentially employed to address each problem step by step. The process involves three key steps: first, identifying whether idioms are in their literal or figurative form; second, transforming them if necessary; and finally, usingthe final form to rank the input images.

Table Question Answering (TQA) involves extracting answers from structured data using natural language queries, a challenging task due to diverse table formats and complex reasoning. This work develops a TQA system using the DataBench dataset, leveraging large language models (LLMs) to generate Python code in a zero-shot manner. Our approach is highly generic, relying on a structured Chain-of-Thought framework to improve reasoning and data interpretation. Experimental results demonstrate that our method achieves high accuracy and efficiency, making it a flexible and effective solution for real-world tabular question answering.