We describe our system for ChildSafeAds 2026, the shared task on commercial content in child-facing YouTube videos: it labels each sponsored segment for commercial type (ST1), product category (ST2) and EU compliance flags (ST3). A multi-task ModernBERT-large encoder and an entailment-style reformulation of the compliance subtask supply seed-averaged probabilities, merged label by label under a paired bootstrap and a dev-to-test count check. Inference uses eight fine-tuned encoders and no generative model; the 503 test instances are estimated at under one GPU-hour, and all experiments cost about US17. It scored 0.7645 mean macro-F1 on development and 0.6798 on test, 0.046 of it from a rule keyed on advertiser identity for one ST1 class absent from development. Our official entry, a variant using one leaderboard probe, scored 0.7022, fourth of 21 teams. A single-seed ablation values the four access levels at +0.042, against +0.128 from encoder scale. We also report five negative results, among them seed variance matching our largest modelling gains, synthetic data helping one architecture and breaking the other, and 21% more training data lowering the test score; separately, near-duplicate training instances agree on the full flag set only 43–63% of the time, often on identical cited evidence.