However, the conducted experiments show that ICL with LLMs displays moderate entity recognition performance overall. Considering the limited number of demonstrations provided, i.e., only ten per entity type, the results of GPT-4 are promising nonetheless. For the fine-grained distinction of domain-specific entity types, a strong influence of domain shift was observed, where few-shot demonstrations from a slightly shifted domain caused a major decline in performance. It was also observed that the performance degraded for rather domain-specific entity types. For instance, ICL exhibits proficiency in identifying general entity types such as EnvironmentalTestParameter, ChemicalElement, Value, etc. However, the performance of ICL is suboptimal on entity types which are more specific for the materials mechanics domain like Damage and Defect, etc. It was also demonstrated that GPT-4 consistently outperformed GPT-3 for all entity types in both domains. This indicates the benefit of larger foundation models in conjunction with a larger and more comprehensive pre-training corpus. In conclusion, for tasks which do not need particularly fine-grained distinction of domain-specific entities, an ICL approach could be practical. This holds especially for the low data quantity scenario where only few annotated entities are available for each entity type. When annotated data is scarce, approaches can be envisioned which use latest retrieval strategies based on vector searches to select a few representative sentences for the target corpus for subsequent annotation and few-shot prompting.