The first detection of astrophysical neutrinos and subsequent investigations into their origins by IceCube have unveiled a new window into the extreme universe. As we look to the next generation of neutrino telescopes, such as TRIDENT, the ability to detect all three flavors of neutrinos will shed light on the mechanisms responsible for their production within these astrophysical sources. Moreover, this comprehensive detection capability will serve as a valuable tool for exploring new physics phenomena. Notably, IceCube's observation of tau neutrino candidate events has already showcased the significant potential of PMT waveforms in event identification. In TRIDENT, we aim to record multi-channel waveforms from PMTs within the Hybrid Digital Optical Modules (hDOM), providing powerful tools for tau neutrino identification. In this study, we present the current simulation pipelines implemented in TRIDENT and share preliminary results on the classification efficiency of tau neutrinos using Graph Neural Networks.