Newsrooms handle a plethora of incoming stories. Gathering relevant background information in a time-efficient way is a core challenge faced by journalists. We propose a knowledge graph-based RAG system to suggest background information relevant to an incoming news story. We use Large Language Models to construct a knowledge graph leveraging multiple web-based information sources, from which the background information is retrieved upon query. Using human annotation and specifically designed metrics, we evaluate and compare the retrieved information using different LLMs. We further report the evaluation done by the target users of the system (journalists in a local newsroom). Thus, the research provides implementation and evaluation of a novel application of knowledge-graph-based RAG for the retrieval of background information for a news story.