We present a reference-based evaluation framework for CRAI-Bench, Task 2 of ImageEval 2026. The method uses GPT-5.4 to extract cultural evidence from the reference image, caption, and generated image. This evidence is converted into metric-specific numerical features and mapped to CRAI scores using a small regressor trained on the released data. On the development set, the proposed approach improves composite Spearman correlation over direct GPT-5.4 scoring, with strong gains on certain metrics. We also analyze performance across caption versions and show that evaluation becomes less reliable for some dimensions as captions become more generic.