Knowledge base construction requires complete answer sets, not just one plausible fact. We present MuseList, a closed-book system for the six-relation AKBC Shared Task 2026. A single open-weight 29.8B-parameter Muse Glimmer model generates candidate answers in several formats, such as recipient lists, recipient–year tables, and venue records. All such records are model-generated; the system retrieves no external documents. Relation-specific rules combine these candidates, select numerical answers using token-level confidence signals, and check proposed reciprocal borders with three separately prompted roles. The complete system scores 0.6700 hidden-test macro-F1 and ranks fourth among twelve systems. Its development validation score is 0.69160; this split informed method selection and is not an untouched evaluation. We describe the method and release an archive that reconstructs the scored submission exactly. Fresh-call support is separate and partial: it regenerates later-stage responses, not an end-to-end submission.