Background: Public facing large language models (LLMs) are increasingly used for health guidance, including triage recommendations. We evaluated whether augmenting LLM prompts with standardized clinical concepts from the Unified Medical Language System (UMLS) could improve the safety and robustness of clinical triage recommendations. Methods: We used a publicly available dataset comprising 60 clinician-authored clinical vignettes, each represented in 16 demographic and narrative variations, yielding 960 vignette-factor combinations. Clinical entities were extracted using a two-stage pipeline combining ClinicalBERT-based named entity recognition with rule-based identification of laboratory abnormalities. Extracted entities were mapped to UMLS Concept Unique Identifiers (CUIs). Negated concepts were excluded. A confidence-weighted CUI voting classifier was trained using empirical associations between CUIs and clinician-assigned triage categories. We compared five approaches: CUI-only classification, MedGemma 27B, MedGemma 27B augmented with CUIs, GPT-4o-mini, and GPT-4o-mini augmented with CUIs. Outcomes included overall accuracy, under-triage, over-triage, emergency-case accuracy, and sensitivity to anchoring statements. Results: CUI augmentation decreased under-triage but increased over-triage in both models tested (GPT-4o-mini and MedGemma 27B). It improved high-acuity recognition while reducing recognition of low-acuity cases. CUI augmentation had mixed effects on overall triage accuracy; accuracy increased for MedGemma 27B but decreased for GPT-4o-mini. Emergency-case accuracy improved from 73.0% to 80.7% for GPT-4o-mini and from 60.5% to 68.5% for MedGemma 27B. CUI augmentation also reduced susceptibility to anchoring statements. These findings suggest that the principal value of CUI augmentation may be shifting model behavior toward safety-oriented behavior rather than uniformly improving overall accuracy. Conclusion: Ontology-grounded prompt augmentation shifted LLM triage recommendations toward greater sensitivity to high-acuity presentations and reduced overall under-triage. These safety gains were accompanied by increased over-triage and mixed effects on overall accuracy. A hybrid architecture combining LLM-based language understanding with interpretable UMLS-derived clinical concepts may improve the safety and robustness of AI-assisted triage. Further evaluation using real-world patient communications and clinical outcomes is warranted.