We describe our AKBC 2026 closed-book knowledge base construction system: Gemma-3-27B-pt, raw text completion, 20 candidates per query, and relation-specific aggregation without an arbiter. It uses 27.4B parameters and scores 0.6670 row-averaged Macro F1 on the official test, placing sixth. Experiments distinguish this final pipeline from an earlier Mistral system with an 8B arbiter. Backbone and access format outweighed prompt-level interventions: under matched prompt formats, the pretrained checkpoint exceeds its instruction-tuned sibling by 0.11 on validation. Numeric example configurations primarily shift retrieval; procedural wording with five added JSON examples changes the selected answers by only one row. Verification instructions can shift rejection. A biography-first city prompt format affects both, raising validation F1 from 0.55 to 0.61 at a fixed vote gate. Gating, information outside the model, and seed stability help interpret transfer. These findings have counterexamples; leaderboard comparisons describe development outcomes, not an untouched final evaluation.