We present CITAR-NER-Br, a unified NER dataset for direct extraction of fine-grained legal citation subcomponents in Brazilian Portuguese. CITAR-NER-Br annotates six citation subcomponents over 270 legal documents from LeNER-Br and Ulysses Tesemõ, capturing the overlapping and discontinuous spans intrinsic to legal citations. To the best of our knowledge, this is the first unified NER dataset for the legal domain specifically designed to represent discontinuous entities, which account for 44.8% of annotations, the highest proportion among existing legal NER corpora. We establish baselines using four configurations across three LLM-based and non-LLM NER methods to support future research. Our experiments demonstrate that, while non-LLM methods achieve strong performance, they remain heavily reliant on high-quality training data. Conversely, LLM-based methods achieve overall lower performance, but require only few-shot supervision. Moreover, the traditional exact-match NER evaluation approach significantly penalizes LLM-based methods even when their outputs preserve semantic accuracy.