GraphRAG is a popular way to use knowledge graphs to ground AI apps in facts. Most GraphRAG tutorials use LLMs to build graph automatically from unstructured data. However, what if you're working on use cases such as investigations and sanctions compliance -- "catching bad guys" -- where transparency for decisions and evidence are required?

This talk introduces how investigative practices leverage open data for AI apps, using _entity resolution_ to build graphs which are accountable. We'll look at resources such as _Open Sanctions_ and _Open Ownership_, plus data models used to explore less-than-legit behaviors at scale, such as money laundering through anonymized offshore corporations. We'll show SOTA open models used for components of this work, such as _named entity recognition_, _relation extraction_, _textgraphs_, and _entity linking_, and link to extended tutorials based on open source.