Tomaz Bratanic from the Neo4j team discusses the advancements in extracting structured information from unstructured text using Large Language Models (LLMs), highlighting how the process has become more accessible thanks to these models. The article explores constructing a knowledge graph from a sample Wikipedia page using OpenAI functions and LangChain, emphasizing best practices and limitations of current LLMs. It outlines an information extraction pipeline that includes steps like coreference resolution, named entity recognition, and entity disambiguation, and demonstrates how a LangChain-based setup can connect to a Neo4j database to build the knowledge graph. The text also underscores the importance of defining graph schemas and the entity disambiguation step for accuracy, while showcasing how to query the graph using Cypher statements in a Retrieval-Augmented Generation (RAG) application. The author concludes by inviting readers to learn more about AI applications with graphs at the upcoming NODES conference organized by Neo4j.