This work assesses whether the domain-and task-agnostic characteristics of foundation LLMs can be exploited to alleviate the data sparsity issue in the materials science domain. In particular, we study domain-specific NER to evaluate how reliably LLMs can assign materials-specific entity types, which are explicitly defined in a materials domain ontology, to textual entities. Primarily, we compare foundation models in a few-shot incontext learning (ICL) setting with fine-tuning task-specific language models. The comparison covers conventional fine-tuning of multiple pre-trained BERT variants, their LoRA counterparts, and two GPT LLM-variants. The utility and potential of these distinct methodologies in materials science are discussed. The considered data covers CPMP relationships and has an emphasis on materials fatigue. The wide range of influence factors in materials fatigue establishes the demand for fine-grained recognition of many specific entity types. This is especially challenging for allpurpose LLMs as it requires distinction of very domain-specific entity types which hold a similar meaning, yet are potentially rather underrepresented in the LLM's pre-training corpus. To retrieve all relevant influence factors of materials fatigue, considering fulltexts rather than only abstracts is imperative. Another objective is Fig. The figure depicts the workflow followed in this work, where the arrows indicate the flow of data. Two sets of publications were segmented into sentences, encoded using an embedding model, indexed using a vector store, and partitioned into train, validation, and test sets. These subsets were then used for fine-tuning specialized NER models in branch A and within a few-shot LLM prompting approach in branch B as indicated by the arrows. The former approach A does not utilize the sentence embeddings. In the latter approach, B1 indicates the sequential sampling of target sentences from the test or validation set and B2 the fetching of k 1 -closest few-shot demonstrations from the training set. Both are used to populate a detection prompt template which is then passed to the LLM to obtain class-annotated entities for the target sentence (B3). Subsequently, for each class within the set of candidate classes C val predicted for the same entity during detection, a set of k 2 -closest few-shots are fetched (B4) and then passed to the LLM for validation (B5).