prepare-training-corpuslisted

# Prepare Training Corpus Make the corpus reproducible before any expensive labeling or training call. ## Define the contract Write down: - the population and approved data sources; - the unit of an example and the group that must never cross splits; - target split sizes and distributions; - input, output, abstention, and identifier schemas; - required positive, negative, ambiguous, and boundary cases; - leakage rules for prompts, labels, canonical IDs, and teacher-only metadata. Separate deterministic scenario generation from teacher labeling. Materialize and validate the entire candidate pool without model calls first. ## Materialize deterministically 1. Pin source snapshots and generator revision. 2. Set and record the random seed. 3. Assign splits by stable group identity, not by individual row. 4. Write a selection manifest before calling a teacher or evaluating a model. 5. Keep opaque request-local identifiers in model-visible inputs when canonical IDs would leak answers. 6. Store teacher/reference fields outside the model prompt. Do not silently repair generated or teacher-produced rows. Preserve raw failures and derive validated outputs separately. ## Validate and freeze Use `scripts/build_corpus_manifest.py` for JSONL split invariants: ```sh python scripts/build_corpus_manifest.py \ --split train=path/train.jsonl \ --split validation=path/valid.jsonl \ --split test=path/test.jsonl \ --id-field id --group-field deckGroupID \ --output path/corpus-m