In addition to addressing challenges, the practical implications and applications of semi-automated annotation for structured JSON and summaries are profound. One notable advantage is the potential to significantly reduce the financial and labor costs associated with manual annotation efforts. By combining human expertise with the capabilities of language models, our workflow streamlines the annotation process, enabling efficient extraction of structured information from large volumes of text data. Furthermore, the semi-automated nature of the workflow minimizes the risk of errors commonly associated with manual annotation, ensuring the production of high-quality annotations suitable for various downstream applications. Overall, the adoption of semi-automated annotation holds great promise for accelerating research in materials science by facilitating the creation of comprehensive and machine-readable datasets essential for advancing scientific discovery and innovation.