Add retrieval-determinism section; correct the regeneration instructions

v1.0.1-addendum.md

@@ -88,14 +88,55 @@ functional subset (1,429/1,743 test questions), and the interval × stale cell
 
 ---
 
+### 3. Retrieval determinism: two rules a reimplementation must match
+
+Neither of these changes any published number, but both are load-bearing if you
+rebuild the reference retriever and expect to reproduce our figures.
+
+- **Tie-breaking is decided by rule, not by score.** As §2 records, 1,429/1,429
+  (100.0%) of functional stale pairs are *exact* BM25 ties, because the validity
+  window is not a lexical feature. The tie-break rule therefore decides every
+  stale-versus-gold ordering on its own. Ours breaks ties
+  **reverse-lexicographically on the rendered evidence line**, everywhere. An
+  earlier version of the metric scripts instead used a stable sort that preserved
+  candidate-pool (CSV) load order; the two paths were reconciled to the deployed
+  behaviour, and the reverse-lexicographic rule is what produced every number in
+  the paper and in `baselines/`.
+
+- **The non-oracle anchor linker is deterministic.** The simple
+  longest-substring linker over KG entity labels (gold-anchor recall 60.2%) was
+  previously sensitive to `PYTHONHASHSEED` through set-iteration order. It is now
+  deterministic. The published non-oracle 2-hop 3+-hop CCR moves **0.077 →
+  0.070** as a result. This is the same method made reproducible, **not** a
+  correction to a result: no claim, comparison or conclusion depends on it.
+
+---
+
 ## Regeneration
 
-From the code release, `code/` directory, CPU-only:
+**The benchmark itself is fully regenerable** from the code release (`code/`,
+stdlib-only, CPU-only). See `code/README.md`; you will need
+`tkgl-smallpedia_edgelist.csv` from TGB 2.0, which we do not redistribute.
 
 ```
-python camera_ready_tables.py --leak    # leak mechanism + EM with/without interval
-python camera_ready_tables.py --neg     # negative-hit-rate + BM25-tie analysis
-python eval_negatives_ranking.py        # rebuilds the negatives-ranking artifact (~4 min)
+python build_benchmark.py --kg_path <tgb-csv> --output_dir ./out/ \
+    --target_n 10000 --seed 42
+```
+
+**The interval leak (§1) is checkable directly from the shipped data**, with no
+retriever and no extra scripts — for every interval question, `t_query` equals
+the gold answer year:
+
+```python
+import json
+rows = [json.loads(l) for l in open("benchmark/benchmark_labelled.jsonl", encoding="utf-8")]
+iv = [r for r in rows if r["operator_type"] == "interval"]
+print(len(iv), sum(str(r["t_query"]).startswith(str(r["answer"])) for r in iv))
 ```
 
-Split counts derive from the `split` field of `benchmark_labelled.jsonl`.
+**The retrieval-side numbers (§2, §3, and the paper's tables) are *not*
+regenerable from this release.** They require the reference retriever and
+generator, which are not included here. They are instead shipped as *outputs*:
+every figure behind those claims is in `baselines/`, so each is checkable
+without re-running anything. Split counts derive from the `split` field of
+`benchmark_labelled.jsonl`.