feat: phase 3 of AI pipeline hardening — extraction quality

Replace stateless one-shot extraction with a stateful, paced, cancellable
pipeline. Six subtasks:

- 3.1 Sentence-aware chunking with 800-char overlap (was paragraph-only
  at 4000 chars). Hard-split fallback for runaway sentences.
- 3.2 Stateful extraction: chunks 2+ receive an "already-extracted topic
  IDs" hint capped at 200 IDs, so the model reuses IDs instead of
  inventing variants like software-developer vs software-engineer.
- 3.3 Token-bucket limiter in llmRetry.js (extractionLimiter, 5 req/min).
  callLLM awaits the limiter before fetch; 429+Retry-After calls
  pauseUntil. Replaces hard setTimeout(12000) and setTimeout(15000).
- 3.4 relevance_locked column on topics — admin edits to relevance are
  sticky across re-extraction. Migration + merge respects the flag +
  unlock checkbox in KnowledgeGraph edit form.
- 3.5 Unify relation vocabulary — handbook prompt no longer mentions
  legacy "executes"; one-shot migration rewrites existing executes rows
  to executed_by with source/target swapped.
- 3.6 Cancellation — Cancel button on UploadZone wired to an
  AbortController threaded into callLLM; aborted runs persist status =
  "cancelled" rather than "failed".

Tests: 16 new unit tests for chunkText, buildKnownIdsHint, and
createLimiter. All 61 tests pass, 0 lint errors, build clean.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

src/lib/db.js

@@ -18,6 +18,7 @@ export async function saveTopics(topics) {
       type: t.type,
       description: t.description,
       learning_relevance: t.learning_relevance || 'standard',
+      relevance_locked: t.relevance_locked === true,
     }, { requestKey: null });
   }
 }