Pretrained language models hold factual knowledge in their parameters, and the LM-KBC challenge measures how much of it can be turned into a knowledge base: given a subject and a relation, a system must return every object that completes the pair, and nothing at all when there is none. The 2026 edition forbids any lookup and caps all models used together at 32B parameters. Because the model may be chosen freely for the first time, a system has three things to decide: which model to run, how to put the question to it, and how to choose an answer from what comes back. We propose MAVE, which puts each item to one open-weight 24B model as several different questions and gives each question one vote. Each of the six relations applies its own rule for how much agreement it takes before an object is emitted. It reaches an official test macro-F1 of 0.6908, which places it second on the final leaderboard.