Cloud computing and DevOps have made microservices a common architecture for scalable, maintainable software systems. However, migrating monoliths to microservices remains challenging due to tight coupling and unclear service boundaries. Existing decomposition approaches typically rely on either structural dependencies or semantic similarity signals, but rarely integrate both within a unified representation learning objective. This paper proposes TripleBound, a hybrid framework for automated monolith to microservices decomposition that augments a heterogeneous graph neural network with weakly supervised triplet constraints derived from parser inferred service groups. Rather than combining independently trained structural and organizational representations after training, TripleBound injects triplet-based constraints directly into the shared structural latent space, enabling both signals to be jointly optimized during representation learning. Structural dependencies are captured using CHGNN, which models the monolith as a heterogeneous graph with program nodes, resource nodes, CALL edges, and CRUD edges. Semantic relationships are incorporated through triplet constraints generated from service groups inferred by a parser using cues such as package structure, naming conventions, and code location. A dynamic weighting schedule progressively shifts emphasis from structural reconstruction toward semantic consistency as training proceeds. Evaluation on AcmeAir, DayTrader, PlantsByWebSphere, and JPetStore shows that TripleBound achieves the highest composite decomposition score on AcmeAir and DayTrader compared to CHGNN and MonoEmbed baselines, while CHGNN remains stronger on PlantsByWebSphere. On JPetStore, TripleBound improves SM, IFN, and ICP over CHGNN with equal NED. Per-metric analysis reveals tradeoffs: gains in structural modularity and inter-partition coupling are accompanied by higher entity distribution imbalance on some datasets.