Our primary task involved annotating materials science text to generate datasets of structured JSONs, aiming to convert unstructured content into a machine-readable format. The structured JSON extraction task was meticulously guided by a detailed prompt, allowing the model to transform individual excerpts into coherent and well-organized JSON files, capturing vital information such as chemical compositions, processing conditions, characterization methods, and performance properties. As our main focus, this task provided a foundation for creating a valuable resource for materials informatics. Additionally, recognizing the token size limitations of LLMs when handling lengthy input texts, we extended our efforts to include an abstractive summarization task. While the summaries weren't used for our primary objective, they can serve as a fallback strategy for cases where the token capacity of the LLM is exceeded, showcasing the versatility of our approach. Leveraging the NOUGAT tool for parsing PDF files, we efficiently obtained text data in markup format from literature, facilitating our annotation endeavors.