Knowledge-driven decision support in complex temporal systems requires recurrent structures that are both interpretable and computationally tractable, including ordinal dynamics, cross-variable dependence, and lead–lag organization. We propose MOPP-Miner, a structure-constrained framework for sampled high-coverage discovery of lag-labeled multivariate order-preserving offset patterns. MOPP-Miner encodes each sampled window as an ordinal-correlation-lag triplet and processes it through Stage-1 lag-bounded construction with synchrony gating, Stage-2 key-local consolidation, and Stage-3 support-aware validation. Across target-aligned synthetic, archive, financial, and preregistered topology benchmarks, the reported evidence supports validated multivariate recovery, directed-structure reconstruction, calibrated tractability, operating-boundary robustness, and financial interpretability, with downstream utility treated as secondary supporting evidence. All claims are scoped to sampled high-coverage discovery under the paper’s benchmark object and mapping protocol rather than to exhaustive full-space recovery or universal ranking across heterogeneous temporal-discovery objectives.