We describe a system for the LM-KBC 2026 shared task on closed-book knowledge base construction. Given a subject and relation, the system tries to predict all corresponding objects without retrieval or additional training and remains within the 32-billion-parameter limit. The system uses the base model gemma-4-31B with separate prompting and aggregation rules for six relations; few-shot demonstrations and selection thresholds come from the train set and are checked on the validation set. It obtains 0.6975 global F1 on the official validation set, compared with 0.308 for the organiser baseline on validation. The largest per-relation gains over our earlier baseline configuration come from a company-listing prompt with explicit abstention and a log-stratified area prompt, plus a vote-share abstention threshold for empty-heavy relations (like personHasCityOfDeath) that recovers most of the empty-case precision. Across more than 30 prompting, verification, and aggregation configurations, further inference rarely recovers facts that are absent from the generated candidates. These results indicate that relation-specific elicitation improves closed-book extraction, while candidate coverage and selection remain the main limitations. Code, prompts, and configuration files are available at https://github.com/ruggsea/lmkbc2026-submission.