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.

Legal documents frequently contain heterogeneous references to judicial precedents, making automated Precedent Reference Extraction (PRE) challenging for legal AI systems. Existing approaches rely heavily on LLM-based pipelines, which are often effective but also incur recurring prompting costs in high-volume production workloads and struggle with tracing extracted entities back to their character offsets in the source text. In light of these issues, we present Notarius, a domain-specific and production-ready hybrid framework that decomposes PRE into two stages: Overlapped NER Feature Extraction (ONFE), which extracts fine-grained precedent features, and Precedent Feature Aggregation (PFA), which reconstructs complete references for downstream applications. Our implementation integrates the Unified NER method W²NER with domain-specific heuristics. We evaluate Notarius at both feature and reference levels on a collection of judicial decisions and compare it in production against an LLM-based pipeline. Notarius achieves higher extraction quality, eliminates recurring LLM API costs for PRE, and provides span-level offset traceability. The system currently powers precedent extraction on Jusbrasil, demonstrating that discriminative, domain-specialized NER architectures remain valuable for cost-effective, high-precision legal information extraction in the LLM era.