Explicit subspace disentanglement, enforcing orthogonality and simplex constraints on learned projections, is an increasingly common recipe for paraphrase models claimed to separate lexical, syntactic, and semantic information. A dual-encoder hierarchical model trained with these constraints on the DiPPer corpus of LLM-generated paraphrases produces near-perfect geometric separation, a pairwise Centered Kernel Alignment (CKA) of roughly 0.0007, yet that separation is not evidence of functional linguistic specialization. Three results establish this. First, near-zero CKA is largely preordained by an explicit orthogonality penalty, which converges to 4×10^-6. Second, each of the three subspaces, including the nominally syntactic one for which we identify no functional probe, classifies paraphrase in isolation at F₁ above 0.986, so the subspaces are not functionally distinct. Third, removing disentanglement at matched capacity costs only 0.013 F₁, leaving most of the reported gain attributable to dual-encoder capacity rather than disentanglement. Capacity-matched ablations and controlled functional probes, not CKA alone, are required to substantiate disentanglement claims; orthogonality penalties otherwise manufacture the very metric used to certify them.