Imagine trying to understand a spreadsheet filled with cryptic codes and jargon. That's essentially what Large Language Models (LLMs) face when dealing with raw data. They're powerful, but they lack real-world context. Enter Knowledge Graphs – the missing piece of the puzzle. Researchers have developed RACOON, a clever system that connects LLMs to Knowledge Graphs, giving them access to a vast network of facts and relationships. Think of it as giving the LLM a cheat sheet to decode the data. RACOON works by first identifying entities in a dataset, then using a Knowledge Graph to retrieve related information. This extra context makes the LLM much more accurate at labeling and understanding the data, like correctly classifying "UEFA Champions League" as a "time.event." The results are impressive: RACOON boosts accuracy by up to 21% compared to LLMs working alone. This improvement is crucial for various data tasks, from finding insights in complex tables to automatically labeling messy spreadsheets. While RACOON showcases the potential of this approach, challenges remain, particularly in ensuring the accuracy of linked entities. As knowledge graphs and LLMs both evolve, we can expect even smarter systems that turn raw data into valuable insights.