Legal natural language processing (NLP) resources often flatten judgments into full texts paired with summaries or outcome labels, despite appellate reasoning being organised around facts, issues, authorities, analysis, and dispositions. We introduce , a public dataset and annotation pipeline that converts 45,020 Indian Supreme Court appeals into FIRAC-style case briefs with metadata, leakage-controlled Facts, typed Rules/Authorities, Analysis, Conclusion and outcome labels. This structure supports headnote generation, issue spotting, authority recommendation, reasoning-aware audit, and leakage-aware outcome prediction. Human legal evaluation, large language model (LLM) screening, summarisation comparisons, and downstream baselines demonstrate the dataset’s usability beyond conventional summarisation or outcome-only legal judgment prediction, providing a practical foundation for more structured and inspectable legal NLP.