to realizing the full potential of LLMs. Manual annotation, a labor-intensive process, emerges as a bottleneck in this context. Relying solely on human annotation proves both expensive and time-consuming, while exclusive dependence on LLMs through few-shot learning risks errors due to the models' tendency to hallucinate. The urgent need for innovative annotation approaches is evident, aiming to facilitate the automation of pipelines that unlock the wealth of information embedded in scientific literature.