Further, special cases were observed in the token-level predictions where the correct entity type was predicted but the predicted span differed from the ground truth span. Such errors constitute a larger fraction of total predictions (12.13%) in ICL of GPT-4 compared to the best fine-tuned task-specific model MatSciBERT (10.64%). An example is illustrated in Figure example #7, where the in-context GPT-4 predicts the span of the ground truth entity 'trapped dislocation' wrong but predicts its type correctly. Similarly, in sentence #8, GPT-4 gets both type and span wrong for the ground truth entities 'coherent twin boundaries' and its abbreviation 'CTB'. On the other hand, the fully fine-tuned MatSciBERT model could predict the span correctly ignoring the parenthesis of the abbreviation. These examples are instances showing specific annotation guidelines in action, which are not guaranteed to be present in the few-shot demonstrations and hence ICL might not adhere to these guidelines. Furthermore, there is a wide variety of acronyms and chemical symbols that may be used without its expansion in the example sentences, for instance 'CTB' in sentence #7. In such cases, approaches which provide further context could be envisioned, e.g., using the vector store for retrieving the full forms of acronyms and agentic approaches, where domain-specific class definitions are automatically retrieved from APIs and supplemented in the prompt. Supplementing such information benefits from linking entity types to an ontology, Such so-called knowledge augmentation has lead to significant performance improvements in the biomedical domain .