The field of entity recognition and data integration is experiencing significant advancements, driven by the development of large language models and innovative methodologies. Researchers are exploring new approaches to improve the accuracy and efficiency of entity recognition, such as prompt-based learning, in-context clustering, and few-shot prompting. These methods have shown promising results in various domains, including medical texts, historical documents, and cultural news texts. Furthermore, the integration of common data elements across heterogeneous datasets is being facilitated by dynamic frameworks that leverage embeddings and clustering techniques. Noteworthy papers in this area include: Segmenting France Across Four Centuries, which introduces a new dataset for analyzing long-term land use and land cover evolution. Research on Medical Named Entity Identification Based On Prompt-Biomrc Model and Its Application in Intelligent Consultation System, which presents a novel approach to medical entity recognition using prompt learning. In-context Clustering-based Entity Resolution with Large Language Models, which proposes a scalable and effective method for entity resolution using in-context clustering.