The prompt that we use at the detection stage is shown in Figure . At a high-level, the prompt consists of three parts, namely, task-specification, few-shot demonstration, and a target sentence. We use the OpenAI's chat completion API endpoint and adhere to the required format in the JSON bodies. The request body in their JSON format is partitioned into multiple roles, (i) system, (ii) user, and (iii) assistant which are each used to convey spe-Fig. This figure shows the structure of the validation prompt. In the validation prompt, the LLM is requested to disambiguate entities which have been annotated more than once during the detection stage. The validation prompt is subdivided into three sections -the task description, the few-shot demonstration, and the target sentence. Furthermore, the figure indicates the JSON format used by many OpenAI models where both role and content are defined. Depending on the intended behaviour of the LLM, messages can be conveyed through different roles. For brevity, not all few-shot demonstrations are shown. cific information to the LLM in a structured fashion . The system role initiates the conversation by defining a persona and role which the LLM is supposed to fulfill. In our system message, we provide information about the domain of interest to the model to incentivize it to rely on the relevant portion of its latent "knowledge". We use an XML tagging approach where classes of interest are referenced and demarcated in the prompt as XML elements since LLMs can understand such syntax to detect instance boundaries 64,66 . The user message within the task description section provides the target class to be detected and instructs the model to consider the subsequently provided few-shot examples. The most relevant few-shot examples from the train set are fetched based on their semantic similarity to the target sentence embedding vector from either the validation or test set. This is performed through an approximate nearest neighbor (ANN) search based on the Euclidean distance (L2-norm) metric which approximately identifies and retrieves the k 1 -nearest neighbors, where k 1 =10. The ANN search is restricted to the partition within the vector store that contains sentences with annotated instances of the target class. The few-shot demonstrations are crucial as they serve as a means to provide related domain-specific examples to the LLM which is otherwise unaware of our class definitions and annotation policies. The input and output of few-shot pairs are provided through messages alternating between user and assistant roles, respectively. This is in line with OpenAI recommendations . Finally, we provide the target sentence from the validation or test set as a user message, inducing the LLM to generate an annotated version as output.