In contrast to the fine-tuned BERT models, which perform multi-class token classification, during ICL, we decompose the problem into multiple binary classification tasks where each foreground class is detected individually. This strategy is adopted from Wang et al. and their GPT-NER framework, which proposes to frame the NER task, which is a token-level classification task, as a text generation task in order to play to the strength of latest LLMs. This is achieved by bracketing named entities with special tokens @@ and ##. This approach presumably simplifies the task for the LLM, as it only needs to focus on replicating the text while marking the entity locations with the designated tokens. At the detection stage, the binary classification tasks are decoupled from each other. This means that the LLM is unaware of alternative classes when annotating for a specific class. The outcome of this stage is inherently non-flat NER.