We introduce UIEPrompter, a unified, training-free framework that secures 1st place in the ACL 2025 shared competition on universal document-level information extraction. UIEPrompter effectively addresses both named entity recognition and relation extraction without the need for annotated data.Leveraging large language models, UIEPrompter establishes a zero-shot baseline through role-specific prompts, which are then refined via few-shot guidance and constrained output generation prompt to align with competition schemas. Additionally, by integrating outputs from several large language models, we reduce individual model biases, thereby improving overall performance. Evaluated on the competition evaluation dataset, UIEPrompter showcases outstanding performance in document-level information extraction, ultimately securing first place. The implementation code is available on GitHub.