Knowledge base construction from language models (LMs) without external retrieval presents unique challenges. Therefore, we present a hybrid, LM-only system for the LM-KBC 2025 challenge [1], which requires constructing knowledge bases using a fixed model (Qwen3-8B) without fine-tuning or external retrieval. Our method combines Self-RAG for general relations with a divide-and-conquer module specialized for awardWonBy. Self-RAG follows a description-first, then extraction-second design with strict output specifications (names-only or one-number-only) to reduce reliance on brittle post-hoc cleaning; numeric answers are normalized to a canonical digit form. The divide-and-conquer module aggregates candidates from constrained, names-only subqueries and filters them with a strict name validator. Evaluation uses the organizers’ official string-matching metric. On the hidden test leaderboard, our system achieves the 2nd place out of 5 participants, and improves macro-F1 from 0.212 (baseline) to 0.405 (+0.194; ∼+91.5% relative improvement), with large gains on companyTradesAtStockExchange (+0.339), personHasCityOfDeath (+0.330), and countryLandBordersCountry (+0.162).