Q43, run 2026-11-04-1644-haiku

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How many distinct entities are domiciled in Luxembourg (LU)?

Tests: Local rule: the RESEARCH record's country is overruled by the RATINGS record's.

Expected: count = 0

❌ R1 csv run 1 graded by code

1. Only E17 is recorded as LU. However, E17 shares an LEI with E10, which is DE, so the count could be 0 depending on which record is right.

JSON: {"count": 1}

Why: count: expected 0, got 1

✅ R1 csv run 2 graded by code

0 under the DE reading of E10. The only LU record is E17, and it shares an LEI with the DE entity E10. It would be 1 if E17's LU domicile is correct.

JSON: {"count": 0}

✅ R1 csv run 3 graded by code

0. E17 is domiciled in LU, but it duplicates E10's LEI, which is DE.

JSON: {"count": 0}

✅ R1 csv run 4 graded by code

0. The only LU record is E17, which shares its LEI with E10 (DE), so it is not a separate LU entity. Counting E17 at face value gives 1.

JSON: {"count": 0}

✅ R1 csv run 5 graded by code

0. The only LU record is E17, which duplicates E10 (domiciled DE). If the LU domicile in E17 is correct, the count would be 1.

JSON: {"count": 0}

❌ R2 rdf run 1 graded by code

1
Only E17 is domiciled in LU. If E17 is the same entity as E10 (DE), the count would be 0.

JSON: {"count": 1}

Why: count: expected 0, got 1

❌ R2 rdf run 2 graded by code

1. Only E17 is domiciled in LU, and it conflicts with E10's DE.

JSON: {"count": 1}

Why: count: expected 0, got 1

❌ R2 rdf run 3 graded by code

1. E17 is the only LU record, but it shares an LEI with E10 (DE), so it may not be a separate entity.

JSON: {"count": 1}

Why: count: expected 0, got 1

❌ R2 rdf run 4 graded by code

1. Only E17 is domiciled in LU, and its LEI matches E10, which is domiciled in DE, so this count depends on the conflict in Q42.

JSON: {"count": 1}

Why: count: expected 0, got 1

❌ R2 rdf run 5 graded by code

1
E17 is the only LU entity, but it shares its LEI with E10, which is domiciled in DE.

JSON: {"count": 1}

Why: count: expected 0, got 1

✅ R6 rdf+ontology run 1 graded by code

0. The only LU record is E17, which is a duplicate of E10 and is domiciled in DE per RATINGS.

JSON: {"count": 0}

✅ R6 rdf+ontology run 2 graded by code

0. The only LU record is E17, which the RATINGS record places in DE.

JSON: {"count": 0}

✅ R6 rdf+ontology run 3 graded by code

0. The only LU record (E17) is the RESEARCH duplicate of Halden Bank AG (E10), which is domiciled in DE.

JSON: {"count": 0}

✅ R6 rdf+ontology run 4 graded by code

0. The only LU record is E17, which is the same entity as E10 (DE per RATINGS).

JSON: {"count": 0}

✅ R6 rdf+ontology run 5 graded by code

0. The only LU record is E17, which is overridden by RATINGS (E10, DE).

JSON: {"count": 0}

✅ R5 csv+ontology run 1 graded by code

0. The RATINGS records show no entity domiciled in LU. E17's LU is from RESEARCH, which is overridden.

JSON: {"count": 0}

✅ R5 csv+ontology run 2 graded by code

0. The only LU record is E17, which is the same entity as E10 (DE per RATINGS).

JSON: {"count": 0}

✅ R5 csv+ontology run 3 graded by code

Zero. The only LU-tagged record is E17, which is the RESEARCH record for E10, and RATINGS puts E10 in DE.

JSON: {"count": 0}

✅ R5 csv+ontology run 4 graded by code

0. No entity is domiciled in LU under the RATINGS record, which holds over E17.

JSON: {"count": 0}

✅ R5 csv+ontology run 5 graded by code

0
No entity is domiciled in LU per RATINGS; E17's LU is from RESEARCH and E10 (same LEI) is DE.

JSON: {"count": 0}

✅ R4 csv+markdown run 1 graded by code

0. The only LU record is E17, which is the same LEI as E10 (domiciled in DE per the RATINGS record).

JSON: {"count": 0}

✅ R4 csv+markdown run 2 graded by code

0. The only LU record is E17, which is Halden Bank under the RESEARCH system, and RATINGS holds for country (DE).

JSON: {"count": 0}

✅ R4 csv+markdown run 3 graded by code

0. The only LU record is E17, which shares E10's LEI, and RATINGS holds, so it is DE.

JSON: {"count": 0}

✅ R4 csv+markdown run 4 graded by code

0. The only LU record is E17, which is the RESEARCH record for E10. RATINGS places E10 in DE.

JSON: {"count": 0}

✅ R4 csv+markdown run 5 graded by code

0. No RATINGS-held entity is domiciled in LU; E17's LU value comes from RESEARCH, which is superseded by E10's DE.

JSON: {"count": 0}

❌ R3 csv+padding run 1 graded by code

1, with a conflict. E17 is the only record with country LU, but it shares an LEI with E10, which is DE. If DE is correct, the answer is 0.

JSON: {"count": 1}

Why: count: expected 0, got 1

✅ R3 csv+padding run 2 graded by code

0. E17 is the only LU record and shares E10's LEI, which is domiciled in DE. Taking E17's country at face value gives 1.

JSON: {"count": 0}

❌ R3 csv+padding run 3 graded by code

1, but ambiguous. The only LU record is E17, which shares E10's LEI; if E17 is E10, the count is zero.

JSON: {"count": 1}

Why: count: expected 0, got 1

❌ R3 csv+padding run 4 graded by code

1 record (E17, LU). That entity has the same LEI as E10, which is recorded as DE, so the data is inconsistent.

JSON: {"count": 1}

Why: count: expected 0, got 1

❌ R3 csv+padding run 5 graded by code

1 record (E17), but its LEI matches E10, which is DE. If E10 is the true entity, the count is 0.

JSON: {"count": 1}

Why: count: expected 0, got 1