feat(example): add L2 classifications for 823/988 WoN entities (S3.4)

Batch classification via OpenRouter (claude-sonnet-4). 165 entities
remain unclassified due to credit exhaustion; incremental skip means
a follow-up run will complete them automatically.

Type × VSM matrix (823 entities):
                  S1   S2   S3  S3*   S4   S5
  Element         86   75   58   21   43   32  (315 total, 38%)
  Process         39   42   37   17   67   24  (226 total, 28%)
  Institution      4   12   30   24    .   52  (122 total, 15%)
  Principle        3    7   15    2   43   32  (102 total, 12%)
  Relation         2   14    5    5   22   10   (58 total,  7%)
  Matrix fill: 29/30 cells (Institution/S4 empty — expected)

Metrics updated: type_entropy=2.0936, vsm_type_matrix_cells=29

Also:
- BatchEvaluator gains delay_seconds param for rate-limited providers
- classify CLI gains --rpm option (--rpm 10 for Gemini free tier)
- history.write_metrics_file now handles non-float metric values
  (type_distribution is a dict, was crashing round())
- run_entity_classification forwards delay_seconds to BatchEvaluator
- classify-links and graph commands added by user (entities --by-type,
  graph --format mermaid/dot, classify-links for Relation enrichment)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

markitect/prompts/execution/batch.py

@@ -102,11 +102,13 @@ class BatchEvaluator:
         config: Optional[RunConfig] = None,
         progress_callback: Optional[Callable[[int, int, BatchResult], None]] = None,
         previous_digests: Optional[Dict[str, str]] = None,
+        delay_seconds: float = 0.0,
     ):
         self._adapter = adapter
         self._config = config or RunConfig()
         self._progress_callback = progress_callback
         self._previous_digests = previous_digests or {}
+        self._delay_seconds = delay_seconds
 
     def evaluate(self, items: List[BatchItem]) -> BatchSummary:
         """Run evaluation for all items and return aggregate results.
@@ -116,9 +118,13 @@ class BatchEvaluator:
         the LLM adapter.  Errors on individual items are captured
         without aborting the batch.
         """
+        import time as _time
+
         summary = BatchSummary(total=len(items))
 
         for idx, item in enumerate(items):
+            if idx > 0 and self._delay_seconds > 0:
+                _time.sleep(self._delay_seconds)
             result = self._evaluate_one(item)
             summary.results.append(result)