Handshapes serve a fundamental phonological role in signed languages, with American Sign Language employing approximately 50 distinct shapes. However, computational approaches rarely model handshapes explicitly, which limits both recognition accuracy and linguistic analysis. We introduce a novel graph neural network that separates temporal dynamics from static handshape configurations. Our approach combines anatomically informed graph structures with contrastive learning to address key challenges in handshape recognition, including subtle inter-class distinctions and temporal variations. We establish the first benchmark for structured handshape recognition in signing sequences, achieving 46% accuracy across 37 handshape classes, compared to 25% for baseline methods.