In conclusion, our study introduces a semi-automated annotation methodology tailored for materials science literature, leveraging Google's Gemini Pro language model. This approach offers a promising avenue for structured information extraction, enhancing the efficiency of annotation tasks while ensuring the creation of high-quality datasets essential for training domain-specific language models. By utilizing structured prompts, we have demonstrated the model's capability to accurately extract information and generate concise summaries from complex scientific texts. Our work highlights the potential of semi-automated annotation workflows in bridging the gap between manual annotation and fully automated processes, paving the way for more efficient and accurate data extraction in materials science research.