Decomposition into multiple binary classification tasks is motivated by the inherent input token limitations of the employed LLMs. Specifically, gpt-35-turbo-0301 and gpt-4-1106-preview were used which have a limit of 4,096 and 128,000 input tokens, respectively, which affects the information that can be provided within a prompt. This renders it impractical to include comprehensive descriptions and demonstrations for all entity types within a single prompt. Furthermore, with exceedingly lengthy prompts and mixed entity type demonstrations, finding relevant information for generation might become increasingly difficult which is commonly denoted as needle in a haystack problem . Consequently, for each input sentence, we construct the prompt N times, with each iteration corresponding to a distinct entity type.