Limitations and future research. A couple of factors related to the design of the ICL prompt workflow could be detrimental to its performance. Firstly, in the detection prompt, each LLM call only asks it to predict entities belonging to a single entity type. Thus, at detection time, the existence of alternative entity types is not indicated by the prompt and consequentially this information is not provided to the LLM. Aside from this, the few-shot demonstrations for the searched entity type are fetched from a partition in the vector store which contains only sentences in which the target entity type occurs. Thus, no negative samples are provided within the prompt. The lack of alternative class mentions and negative demonstrations could incentivize the LLM to predict entity categories that are otherwise less likely to be predicted. In contrast, the fine-tuning approach learns from examples belong-ing to all categories which could culminate in rather well-defined decision boundaries. Moreover, the rule-based approach used for flattening of NER predictions cannot discern which of the individual entity type predictions are more likely since the probability values were not provided by OpenAI's chat completion endpoint at the time of inference. Thus, the rule-based step to resolve overlapped predictions described in the methodology section could end up selecting an incorrect entity type candidate. Rather than flattening the output, prospectively, ICL can facilitate a nested NER approach to do justice to word groups embodying multiple meanings.