Traditionally, RE is formulated as a downstream task to NER and is solved as an ensemble of classification problems for entity pairs. More recent efforts aim to solve NER and RE simultaneously by building end-to-end models. This trend has persisted as pretrained large language models (LLMs) have become more accessible. LLMs have been used for NER/RE tasks in biomedicine, materials, and clinical trials, showing promise as tools for structured data extraction. For example, Dagdelen et al. developed a training pipeline for GPT-3 to extract information from scientific texts about crystalline mate-rials as structured JSON and Walker et al. present an iterative scheme to fine-tune LLMs for extracting structured data of gold nanorods synthesis. Recent studies by Zhong et al. explored fine-tuned LLMs for reaction data extraction from literature in PDF format. The output of these models provides a reasonable coverage of reaction information, with the exception of quantity information. Pretrained LLMs can also be used for this task directly without fine-tuning. For example, a recent preprint by Patiny and Godin explores extracting analytical experiment results from literature solely through prompt engineering. While this method can extract structured data by including in-prompt data schema, it relies on closed-source LLMs and performs poorly when numerical values are involved.