Multi-hop question answering over financial disclosures is often constrained more by evidence retrieval than by reasoning capability. Relevant facts are dispersed across filings, fiscal periods, and peer firms, and noisy long-context inputs degrade reliability even for strong reasoning models. We introduce FinReflectKG–MultiHop, a large-scale benchmark grounded in a temporally indexed financial knowledge graph derived from SEC 10-K filings, containing multi-hop QA pairs spanning intra-document, inter-year, and cross-company reasoning regimes. Questions are generated from statistically informed 2–3 hop typed motifs and paired with provenance-linked evidence to enable controlled evaluation of evidence structure. Across multiple open-weight reasoning LLMs and six structured evidence protocols, KG-linked provenance consistently improves correctness while reducing token usage by over 70% relative to text-window and semantic retrieval baselines. Our results demonstrate that reasoning reliability in finance is fundamentally governed by evidence composition and structure, and that model scaling alone cannot compensate for poorly organized retrieval contexts.