In recent years, Brazil’s federal judicial system has embraced digitalization, making a large amount of legal process information available to citizens and legal experts. Despite the advances, a significant portion of the data produced and stored in legal systems presents itself in the form of natural language text, including numerous petitions and legal decisions. This creates barriers for automated querying and analysis of legal process data, especially considering the importance of the content of legal decisions in these tasks. In this paper, we report on an automated semantic annotation pipeline for judicial decision texts obtained from the official National Uniformization Panel (TNU) jurisprudence website. NLP models are trained in a few-shot learning context with a training set annotated by legal experts. The semantic annotation approach is evaluated using precision and recall. The results of the semantic annotation are produced into RDF-based nanopublications aligned with a reference domain ontology. The annotations are accompanied with provenance information including identification of the machine learning model used.