The U.S. Sentencing Commission provides substantial data about federal criminal sentences but omits judge- and defendant-specific identifiers, as well as many non-incarceration components of sentences. To support research on these gaps, we construct a domain-specific ontology and an annotated dataset from federal sentencing docket entries. These short, contemporaneous records describe counts, incarceration, supervision, monetary penalties, and other sentencing conditions; however, their formatting varies across clerks, judges, and courts. We compare six approaches under a shared labeled-span evaluation: ontology-guided Gemini 2.5 Flash, CRFsuite, ModernBERT, Legal-BERT, zero-shot GLiNER, and fine-tuned GLiNER. On 100 manually annotated docket entries, fine-tuned GLiNER achieves the highest exact-span micro-F1 at .703 ± .009, followed by CRFsuite at .655. Gemini achieves .463 ± .038 exact F1 but the highest relaxed F1 of .787 ± .013 under a diagnostic that credits a prediction only when its label matches and either normalized span text contains the other. The contrast shows that recovering compatible entity text and reproducing ontology-defined boundaries are materially different abilities. The results emphasize the continuing value of domain-specific task definitions, annotation ontologies, and supervised adaptation for legal information extraction. contains the other. The contrast shows that recovering compatible entity text and reproducing ontology-defined boundaries are materially different abilities. The results emphasize the continuing value of domain-specific task definitions, annotation ontologies, and supervised adaptation for legal information extraction.