This paper presents a system developed for the fifth AKBC Shared Task on Knowledge Base Construction from Language Models (LM-KBC 2026). The task involves predicting sets of object-entity for (subject, relation) pairs across six relation types, ranging from single-valued relations to relations with potentially hundreds of recipients. All predictions are generated in a closed-book setting (CITATION), utilizing up to 32 billion parameters across all neural components, without resorting to fine-tuning or retrieval augmented techniques. The development process included two stages: validating the technical methodology using the officially specified baseline model (Qwen3.5-9B), followed by comparing nine open-weight models using the same evaluation framework. This process led to the selection of Gemma 3 27B as the final system. The paper presents the main output-parsing errors identified and resolved, together with an evaluation of self-consistency and majority-voting aggregation for each relation type. The final system improved macro-F1 score from 0.313 to 0.531 (a +70% relative improvement) through workflow optimization and model selection.