Ship classification and maritime applications of computer vision have received a lot of attention yet, despite being a safety-critical field, models utilized remain opaque and therefore untrustworthy. Results on existing maritime datasets highlight a need for vessel-specific explainable techniques due to factors such as vessel variation. We adapt prototypical part-based explanation techniques to handle long-tailed feature distributions which often cause misalignment between human and machine explanations for fine-grained vessel recognition. We introduce CRP-PIPNet to provide more explainable, interactive models that human operators can potentially use for future tasks. We frame the prototype impurity problem as a form of poly-polysemanticity, utilizing gradient-based attribution in conjunction with clustering methods to create gradient cluster centres which act as true prototypes increasing prototype purity. By combining and splitting prototypes, a purer model can be obtained and with cleaner representations for downstream tasks. We validate this with a series of quantitative evaluations from industry-standard resources, Janes’ Fighting Warships, to highlight the potential of achieving greater trust through closer alignment between machine learning models and humans.