We present a hierarchical triplet-based indoor relationship learning method, coined HierRelTriple, with a focus on spatial relationship learning. Existing approaches often depend on attention-based network designs and manually defined training objectives using handcrafted spatial rules or simplified pairwise relationships. However, these methods fail to capture complex, multi-object relationships found in real scenarios, leading to overcrowded or physically implausible arrangements. We introduce HierRelTriple, a hierarchical framework for modeling relational triplets, which first partitions functional regions and then automatically extracts three levels of spatial relationships: object-to-region (O2R), object-to-object (O2O), and corner-to-corner (C2C). By representing these relationships as geometric triplets and employing approaches based on Delaunay Triangulation to establish spatial priors, we derive IoU-based losses between denoised and ground-truth triplets and integrate them seamlessly into the diffusion denoising process. The joint formulation of inter-object distances, angular orientations, and spatial relationships enhances the physical realism of the generated scenes. Extensive experiments on unconditional layout synthesis, floorplan-conditioned layout generation, and scene rearrangement demonstrate that HierRelTriple improves spatial-relation metrics by over 15% and substantially reduces collisions and boundary violations compared to state-of-the-art methods.