The semi-automated annotation workflow presented in this study represents a promising approach for extracting structured information from materials science literature. However, despite its efficacy, several challenges were encountered during the annotation process, highlighting areas for potential refinement. One significant challenge stems from the fact that the language model utilized, Google's Gemini Pro, is not fine-tuned specifically for materials science. Consequently, the model's performance may vary when applied to domain-specific texts, necessitating a substantial amount of human intervention to ensure annotation accuracy. To address this challenge, future refinements could involve finetuning language models specifically tailored to the materials science domain. Notably, our semi-automated approach has the potential to play a pivotal role in this refinement process. By providing high-quality annotated training datasets, our approach facilitates the development of more specialized fine-tuned models capable of accurately extracting information from materials science literature. This iterative loop, wherein our approach enhances its own efficiency and effectiveness, holds promise for continuously improving the semi-automated annotation process and advancing towards the development of fully automated language models tailored for seamless and accurate data extraction from mate-rials science literature.