<p>Entity Resolution (ER) is vital for data integration and knowledge graph construction. Despite the advancements made by deep learning (DL) methods using pre-trained language models (PLMs), these approaches often struggle with unstructured, long-text entities (ULE) in real-world scenarios, where critical information is scattered across the text, and existing DL methods require extensive human labeling and computational resources. To tackle these issues, we propose a Few-shot Uncertainty-calibrated Structural data Enrichment method for ER (FUSER). FUSER applies unsupervised pairwise enrichment to extract structural attributes from unstructured entities via Large Language Models (LLMs), and integrates an uncertainty-based calibration module to reduce hallucination issues with minimal additional inference cost. It also implements a lightweight ER pipeline that efficiently performs both blocking and matching tasks with as few as 50 labeled positive samples. FUSER was evaluated on six ER benchmark datasets featuring ULE entities, outperforming state-of-the-art methods and significantly boosting the performance of existing ER approaches through its data enrichment component, with a 10× speedup in uncertainty quantification for LLMs compared to baseline methods, demonstrating its efficiency and effectiveness in real-world applications.</p>