Financial analysis requires crossing evidence modalities that arrive in incompatible forms — structured market data, reported text (news and regulatory filings), collective expectations from prediction markets, and private portfolio state — which existing systems either handle in isolation or collapse into a single index, destroying the distinction between observed measurements, attributed claims, probabilistic beliefs, and user-specific exposure. We present a continuously running financial NLP system that maintains these modalities natively, linked by canonical entities and time, through continuous knowledge graph construction with per-edge provenance and temporal decay, and answers questions via agentic tool use under a citation contract that is enforced structurally: every citation must resolve to a passage retrieved in the same session, and free-text figures must agree with provider-attributed tool outputs. A learned relevance gate formalises the extraction cost/quality tradeoff, reducing LLM extraction calls while retaining the majority of yield-positive documents. Over 284 benchmark questions executed three times under majority vote, 251 produce non-failing responses (205 strong, 20 pass, 26 weak), 23 fail, and 10 additional questions are data-absent under the evaluation protocol. A structural evaluation framework of eight deterministic pre-judge gates reaches κ = 0.860 with a human annotator. In a sample evaluation, grounding entity descriptions in retrieved passages cuts fabrication from 1.83 to 0.17 unverifiable claims per description.