FIE-sLLM Benchmark v1.0: ontology-constrained decoding for small open language models on clinical nutrition abstracts (code, results, raw outputs)

Companion material for the article Ontology-Constrained Decoding for Small Open Language Models: Structural Gains, Content Trade-offs, and Silent Omission in Clinical Nutrition Abstract Extraction (submitted to IEEE Access, 2026). Six open small language models (Granite-4.0-1B, Qwen3-1.7B, Qwen3-4B-Instruct-2507, Phi-4-mini-instruct, Kanana-1.5-8B, Granite-4.1-8B) were run under four conditions (C0 free generation; C1 schema in prompt; C2 structure-constrained decoding; C3 ontology-constrained decoding with controlled vocabulary) and a rule baseline on 1,658 frozen PubMed abstracts on vitamin D and probiotics. Records were validated in two stages (JSON Schema, closed-world ontology rules) before knowledge-graph insertion and evaluated with gold-free structural metrics, agreement with 386 linked ClinicalTrials.gov registrations, and blinded two-judge human assessment of 360 records. The record contains three archives: (1) code, ontology v1.1 (OWL), extraction profile, controlled vocabulary, derived JSON Schemas, prompts, freeze manifests and corpus manifest (PMIDs and hashes; abstract text is not redistributed); (2) all result tables, paired statistics, per-run metrics, and the human-judgment analysis tables with reproducible code; (3) per-abstract raw model outputs and validation results for every run, KG exports, and ClinicalTrials.gov snapshots. See README.md inside each archive. Ontology v1.0 was first released in an earlier reproducibility package (10.5281/zenodo.20774211); this record supersedes it with ontology v1.1 (adds the hasArm property), a new frozen corpus (1,858 abstracts), and a different benchmark design. Code: MIT. Data and outputs: CC BY 4.0. Copyright holder: ANCHOR Program, Jeonju University; author: Dongwook Han. Funding: This research was supported by the Regional Innovation System & Education (RISE) program through the Jeonbuk State RISE Center, funded by the Ministry of Education (MOE) and Jeonbuk State, Republic of Korea (grant number: 2026-RISE-13-JJU).