Another challenge we faced during the annotation process was the evaluation of the accuracy of the JSON extraction task. The absence of a well-defined metric for evaluating structured JSON extraction hindered our ability to comprehensively assess the fidelity of the extracted data. Developing a robust evaluation metric tailored specifically to the complexities of structured data extraction from materials science literature could be a focus for future work, enabling more nuanced assessments of the model's performance. By addressing this need, future refinements could further enhance the effectiveness and reliability of semi-automated annotation workflows, ultimately advancing the development of fully automated language models tailored for materials science literature.