Nowadays, LLMs continue to grow in model size and are trained with sophisticated training procedures on ever larger quantities of diverse data. This renders toady's language models more flexible and imbues them with the capability of adhering to tasks and demonstrations specified in a prompt. Such models are also referred to as foundation language models and have shown advanced capabilities in generative natural language processing across various tasks including machine translation , question answering , reasoning , spurring the development of autonomous language model systems such as agentic systems . In theory, a major advantage of foundation LLMs is that they are comparatively domain-agnostic and require less tuning towards the target domain and task. This makes them attractive for the materials science field where there is a plethora of tasks, only few of which are supported with sufficiently large annotated datasets. In contrast to foundation models, information extraction models based on conventional language models are typically highly taskspecific . However, it was previously observed that foundational LLMs have difficulties in assigning entity labels in NER reliably, especially in domain-specific settings . This was attributed to the fundamental mismatch between the sequence labeling nature of NER and the primary focus of LLMs on text generation . Generative LLMs excel in predicting the next word in a sequence based on contextual clues, prioritizing fluency, grammatical correctness, and coherence in generated text over capturing subtle semantic clues crucial for accurate entity recognition . Efforts to bridge this chasm between NER and LLMs have encouraged innovative approaches. Researchers have explored strategies such as incorporating explicit entity-tagging instructions into prompts and hybrid architectures combining sequence labeling and text generation .