Efficient architectures for Entity Disambiguation (ED) typically pair a dual encoder for candidate retrieval with a cross encoder for re-ranking. While large language models (LLMs) have recently emerged as powerful alternatives for the ranking stage, their application remains constrained by two primary factors: only a limited number of candidate entities can be provided within the model’s input context, and invoking LLMs for every entity mention leads to prohibitive computational cost.

In this work, we propose SelectED, a confidence-guided hybrid framework for efficient entity disambiguation. Our method uses a dual encoder as a fast primary model and selectively invokes an LLM only for uncertain mentions. A central focus is the systematic study of selective LLM refinement, including uncertainty estimation, thresholding, and prompt design choices for balancing accuracy and efficiency. Experiments on the ZELDA benchmark show that selective prompting improves accuracy by 4.3%, establishing a new best result among directly comparable ZELDA-trained systems while requiring LLM inference for only about 20% of mentions. Moreover, SelectED surpasses full LLM prompting, suggesting that selective refinement can avoid LLM-induced errors on high-confidence mentions.