This paper describes our participation in the CRF Filling Shared Task 2026, which aims to automatically populate a predefined Case Report Form (CRF) from clinical notes describing patients with dyspnea.We propose a two-stage pipeline based on large language models (LLMs). In the first stage, a few-shot prompted LLM extracts candidate CRF fields from the clinical note and outputs them in a structured JSON format. In the second stage, a separate LLM verifies each extracted field against the original note and removes predictions that are not supported by explicit textual evidence. This verification step aims to reduce false positives generated during extraction.Experiments on the development set show that the verification stage significantly reduces unsupported predictions while preserving most correct extractions, resulting in improved macro F1. On the official test set, the proposed system achieves a macro F1 score of 0.56 for both English and Italian. These results indicate that separating extraction and verification can balance recall-oriented extraction with precision-oriented validation in CRF population tasks.