Despite the freedom of information and the development of various open data repositories, the access to legal information to general audience remains hindered due to the difficulty of understanding and interpreting it. In this paper we aim at employing modern language models to extract the most important information from legal documents and structure this information in a knowledge graph. This knowledge graph can later be used to retrieve information and answer legal question. To evaluate the performance of different models we formalize the task as event extraction and manually annotate 133 instances. We evaluate two models: GRIT and Text2Event. The latter model achieves a better score of ~ 0.8 F1 score for identifying legal classes and 0.5 F1 score for identifying roles in legal relations. We demonstrate how the produced legal knowledge graph could be exploited with 2 example use cases. Finally, we annotate the whole Workers’ Statute using the fine-tuned Text2Event model and publish the results in an open repository.