We present nemotron-120b-sitrep, an end-to-end four-stage generate-and-judge pipeline submitted to the First Shared Task on Automatic Situation Report Generation from Crisis Social Media Data at LT4CPR 2026 (AACL-IJCNLP 2026). Powered by NVIDIA’s Nemotron-3 Super 120B model hosted on the NVIDIA NIM API, the pipeline tackles the acute challenges of emergency reporting (social media noise, multi-section hierarchical taxonomies, and strict tweet-to-claim evidence attribution) by decomposing generation into: (1) rule-based ingestion and noise filtering, (2) unified template-filling synthesis across an 11-section situation report (SITREP) taxonomy under strict canonical subsection and evidence-grounding constraints, (3) cross-section deduplication, granular bullet consolidation, and dual-source confidence calibration via a Senior Judge LLM, and (4) Schema v1.2 JSON assembly. On the official shared task evaluation, our system achieves an Official Primary Score of 0.5733 on the 117-cell blind test set (BERTScore F1 of 0.5984 and BLEURT of 0.5483), demonstrating consistent performance that transfers to unseen crisis types, with scores of 0.5011 on the 140-cell training benchmark and 0.5057 on the 23-cell development benchmark. We provide a detailed calamity-by-calamity analysis across disaster scenarios, full algorithmic hyperparameters, and prompt engineering specifications.