The field of Fine-Grained Named Entity Recognition (FG-NER) has received noticeable attention in recent years. Scientific literature, academia, and real-world analysis need fine-grained NER tools to be able to categorize and process a wide range of information and semantics. With the advent of transformer models in the field of NLP, several studies have shown a considerable rise in the performance of transformer-based NER models compared to their prior state-of-the-art methods. In consonance with literature, many of the most performant NER models in this field are limited to coarse-grained entity labels, with fewer than 10 categories. And there is limited research work on classifying named entities into finer and more detailed subgroups.