Entity Resolution (ER) draws connections between scattered, incomplete, and inconsistent records to resolve unique entities. ER is relevant in intelligence analysis, in which organized, complete, and consistent information is crucial for empowering investigations in different areas. This session shows with practical examples the role of the graph as a powerful tool for improving traditional ER. We expose its multiple capabilities: the flexibility of data representation, the application of predictive analysis and machine learning algorithms, and the opportunity to explore the data using network-based visualizations. We show end-to-end pipelines, from data ingestion to downstream analysis, discussing good practices and challenges.