Previous attempts to extract data from literature showcase the potential for automation. Pipelines facilitating automated extraction of compound-property relationships from unstructured battery-specific texts and the extraction of a database of 300,000 polymer property records from 650,000 abstracts exemplify the strides made. Finetuned GPT models have been employed to extract general chemistry information from literature abstracts , demonstrating the versatility of such approaches. Domain-specific pre-training and fine-tuning have proven effective in enhancing transformer-based models' performance in domainspecific tasks . As the emerging large language models (LLMs) are transformer-based, optimizing LLM pipelines for relevant domains becomes imperative, necessitating annotated data for fine-tuning.