Despite the remarkable reasoning capabilities of large language models, they still struggle with one-shot relation extraction without predefined relation labels. We identify two pitfalls: models are often misled by irrelevant tokens instead of relation-conveying semantics, and they often fail to align with the abstraction level human annotators expect. We introduce a novel framework that closes this gap with two components: (1) CogRE, a cognitively-inspired reasoning framework that structures RE into a series of processes mimicking human text-processing; and (2) Hit@Dict, a reinforcement learning intermediate reward strategy that ncourages reasoning to align with relational labels by rewarding relation-relevant phrases in reasoning. The reward is based on a credit dictionary automatically extracted from gold outputs. Our experiments show that our framework improves both accuracy and explanation quality by addressing these two pitfalls. For example, CogRE with Qwen2.5-14B-Instruct on One-shot NYT29 achieves 24.65% F1, surpassing prior reasoning-based designs. Optimizing this approach with RL using Hit@Dict further improves performance by +23.46% points. Finally, human evaluation shows that our best model generates relational phrases closely aligned with gold labels, increasing human explanation quality ratings by 54% (relative).