Access to authoritative financial data does not ensure semantically correct answers: a language model may retrieve a valid observation associated with the wrong series, transformation, unit, or temporal interpretation. We introduce FRED-SemBench, a 200-question benchmark candidate over independently selected Federal Reserve Economic Data (FRED) concepts that separates retrieval failures from semantic data-access failures. We also instantiate Iris, an ontology-backed semantic layer whose versioned concept contracts bind user intent to provider series, transformations, units, temporal semantics, provenance, and declared coverage. A controlled comparison of an unaided model, FRED and Web access, and Iris reveals a reliability trade-off: raw retrieval substantially improves numerical answering while suppressing abstention and reducing clarification accuracy on underspecified queries. Within its pre-declared support set, Iris preserves the numerical benefit, restores clarification behavior, and attains the strongest strict accuracy, suggesting that this trade-off is not intrinsic to data access. These pilot findings motivate evaluating macroeconomic data access jointly in terms of semantic correctness, boundary behavior, coverage, and operational cost.