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A basic task for most Legal Artificial Intelligence (Legal AI) applicationsis Named Entity Recognition (NER). However, texts produced in the context oflegal practice make references to entities that are not trivially recognized bythe currently available NERs. There is a lack of categorization of legislation,jurisprudence, evidence, penalties, the roles of people in a legal process(judge, lawyer, victim, defendant, witness), types of locations (crimelocation, defendant's address), etc. In this sense, there is still a need for arobust golden collection, annotated with fine-grained entities of the legaldomain, and which covers various documents of a legal process, such aspetitions, inquiries, complaints, decisions and sentences. In this article, wedescribe the development of the Golden Collection of the Brazilian Judiciary(CDJUR-BR) contemplating a set of fine-grained named entities that have beenannotated by experts in legal documents. The creation of CDJUR-BR followed itsown methodology that aimed to attribute a character of comprehensiveness androbustness. Together with the CDJUR-BR repository we provided a NER based onthe BERT model and trained with the CDJUR-BR, whose results indicated theprevalence of the CDJUR-BR.

CDJUR-BR -- A Golden Collection of Legal Document from Brazilian Justice with Fine-Grained Named Entities