We describe a system for the AKBC Shared Task 2026. The task is to predict complete sets of knowledge base objects from parametric memory alone, with no retrieval and no fine-tuning, across six relations and within a 32B parameter budget. Our system combines relation-specific multiple prompting, consistency-based aggregation, and error-driven prompt refinement. For each relation, multiple prompt variants and samples elicit complementary predictions, with cross-prompt agreement reducing wording-specific errors and cross-sample agreement filtering unstable guesses. Aggregation is tailored to each relation, including cluster-median aggregation for numeric values and voting for categorical and set-valued outputs. The system achieves a validation macro-F1 of 0.5577 and a test macro-F1 of 0.5699, compared to organizer baselines of 0.3131 (validation) and 0.2964 (test).