Evidence-grounded NLP systems often verify nested text regions sequentially, so verifying one region constrains others. We formalize this as Online Evidence-Containment Prediction (OECP): each candidate is predicted before exact verification and queried at most once. Span containment is exactly a triangular 2D product order, making OECP distinct-query online isotonic regression. With multiple admissible targets, worst-case regret is near-linear, $\widetildeΘ(m_{\mathrm{eff}})$, in the effective scale given by endpoint-rank compression; a single fixed target, as in standard QA, admits an explicit polynomial-time learner with optimal $Θ(\log m_{\mathrm{eff}})$ regret, run on real NQ episodes. On Natural Questions, containment yields a large deductive gain plus a smaller learned gain on unresolved candidates, reproduced across two backbones and DPR hierarchies. Expected Search-space Reduction (ESR) turns these probabilities into decisions, localizing evidence in fewer verifier calls on SciFact, ERASER, and DPR hierarchies; on DPR hierarchies under call budgets it outperforms four recent external selectors and raises fixed-reader F1 over learned and generator-greedy scheduling.

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