Open-Vocabulary 3D segmentation assigns semantic labels to 3D scenes from free-form text prompts. Many approaches fuse multi-view 2D embeddings into a 3D representation, but performance depends on strong cross-view consistency, which degrades under large viewpoint changes and occlusion. This often causes identity mismatches across views and reduces segmentation accuracy. We propose 3D Identity-Enhanced Segmentation (3D-IES), which enforces cross-view identity consistency by aligning 2D instance masks through geometry-based reprojection on a calibrated 3D Gaussian Splatting (3DGS) scene and expanding identities when objects reappear across views. Experiments show that 3D-IES yields a 2.3 mIoU gain on LERF-MASK and achieves comparable results on 3D Open-vocabulary segmentation (3D-OVS) with a 0.6 mIoU improvement, while offering a significant advancement in maintaining cross-view identity consistency.