Corporate ESG reports often contain statements that are vague or difficult to verify, creating room for potential greenwashing. Building automated systems to evaluate such claims is therefore a relevant research direction. Yet, existing analytical tools still show limited ability to verify sustainability promises in multiple languages, especially beyond English. This study examines how large language models (GPT-5) perform in verifying ESG-related promises across Chinese, Japanese, and English reports, aiming to provide a multilingual evaluation baseline. We assess four verification tasks using the PromiseEval datasets [1] in three languages, comparing five prompting strategies from zero-shot to five-shot learning, including Chain-of-Thought reasoning. The four subtasks are Promise Identification (PI), Evidence Status Assessment (ESA), Evidence Quality Evaluation (EQE), and Verification Timeline Prediction (VTP). The five-shot setting achieved the highest overall performance (71.12 % accuracy, 51.92 % Macro-F1). Although the accuracy results appear higher for Chinese (85.12 %) than for Japanese (68.94 %) and English (63.62 %), this mainly reflects class imbalance in the data. Hence, Macro-F1 provides a fairer comparison across languages. Among the four tasks, Evidence Quality Evaluation (EQE) remains the most difficult. While Chain-of-Thought prompting slightly lowers the overall average, it shows selective benefit on the more complex EQE task. Overall, this work offers a clearer multilingual baseline for ESG promise verification and supports the development of language-based tools that enhance the credibility and transparency of sustainability reporting.