Compliance with legal documents related to industrial maintenance is the company’s obligation to oversee, maintain, and repair its equipments. As legal documents endlessly evolve, companies are in favour of automatically processing these texts to facilitate the analysis and compliance. The automatic process involves first, in this pipeline, the extraction of legal entities. However, state-of-the-art, like BERT approaches, have so far required a large amount of data to be effective. Creating this training dataset however is a time-consuming task requiring input from domain experts. In this paper, we bootstrap the legal entity extraction by levering Large Language Models and a semantic model in order to reduce the involvement of the domain experts. We develop the industrial perspective by detailing the technical implementation choices. Consequently, we present our roadmap for an end-to-end pipeline designed expressly for the extraction of legal rules while limiting the involvement of experts.