perf: materialize sections once in resolve_batch and trim before resolution

[SkyeAv]

Summary

resolve_batch re-executed the entire upstream lazy plan — source scan, encoding/regex ops, NLP normalization — once per resolved node column: distinct(...).collect() per column, join_matches' collect per column, and log_unmatched's anti-join collect with --log. A section resolving subject + object + two qualifiers ran that pipeline ~5× (~9× with logging).

Changes

• resolve_batch (fullmap.py) collects the frame exactly once; per-column term extraction, unmatched logging, and join-backs all run against the in-memory frame. Output rows are unchanged.
• join_matches keeps its LazyFrame-in/LazyFrame-out contract over a new eager core (_join_matches_eager) that resolve_batch calls directly.
• trim moves from the finalize ops (_provenance_ops) to just before resolve_batch (_node_ops), so the materialized frame and every downstream join drop the spent raw column_<n> columns early. All readers of those columns run earlier, so this is safe; prune_to_class skips them regardless via its not any(accepts) branch.
• trim's PHASE_OF entry is removed so progress falls back to transform instead of flashing finalize before resolve.

No reordering of filters was needed — every row-reducing op already runs before resolution (pinned by tests/test_lib.py).

Testing

• uv run pytest -q → 928 passed; the 1 failure + 1 collection error are pre-existing environment issues (this venv lacks the [qc] extra — both fail on the clean tree too).
• ruff check, ruff format --check, pyright → clean.
• Synthetic before/after benchmark (50k rows, 4 resolved columns, --log): resolve phase 0.14s → 0.08s (~1.75×), identical output rows. Real tables with heavier encoding chains should gain more, since the eliminated cost is per-column re-execution of the whole upstream plan.

Changelog entry added under Unreleased → Performance.

[coderabbitai]

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