Background/Objectives: Large language models and automated analytical tools show potential for biomedical text understanding, knowledge integration, and single-cell data interpretation, but interpretations of specific gene regulatory relationships still require empirical grounding. For TF–target relationships, one relevant constraint is whether curated regulatory edges show detectable expression-level statistical evidence in the single-cell data being interpreted, because such evidence may vary across cellular states, conditions, and regulatory mechanisms. We therefore evaluated the statistical visibility of known transcription factor (TF)–target relationships in single-cell expression space to aid the interpretation of regulatory inference and automated-tool outputs. Methods: We performed two complementary analyses: first, testing whether curated TF–target edges showed stronger pair-level associations than matched background gene pairs; and second, assessing whether high-scoring TF–gene pairs corresponded to existing regulatory knowledge and whether their target genes showed pathway coherence. Results: Across four peripheral blood mononuclear cell (PBMC) datasets, four adult tissues, and seven adult cell types, curated TF–target relationships showed weak but reproducible statistical visibility rather than strong separation. For all five association metrics, the mean area under the precision–recall curve (AUPRC) was only slightly above the random-ranking baseline of 0.5. High-scoring pairs were more often supported by curated resources, and their target genes showed context-specific Hallmark pathway coherence. Conclusions: Single-cell expression associations can provide useful but limited statistical clues for TF–target regulation and should be interpreted as complementary rather than definitive regulatory evidence.