Triple
T1387168
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saône-et-Loire |
E29871
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Côte-d'Or |
E54900
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Côte-d'Or | Statement: [Saône-et-Loire, borderedBy, Côte-d'Or]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Côte-d'Or Context triple: [Saône-et-Loire, borderedBy, Côte-d'Or]
-
A.
Côte d'Or
chosen
Côte d'Or is a renowned wine-producing region in Burgundy, France, celebrated for its high-quality Pinot Noir and Chardonnay wines.
-
B.
Saône-et-Loire
Saône-et-Loire is a department in the Bourgogne-Franche-Comté region of eastern France, known for its historic towns, Romanesque churches, and Burgundy vineyards.
-
C.
Meurthe-et-Moselle
Meurthe-et-Moselle is a department in northeastern France known for its capital Nancy, rich industrial history, and Art Nouveau architectural heritage.
-
D.
Haute-Saône
Haute-Saône is a rural department in the Bourgogne-Franche-Comté region of eastern France, known for its forests, rivers, and historic villages.
-
E.
Haute-Marne
Haute-Marne is a rural department in northeastern France known for its forests, rivers, and historic towns such as Chaumont and Langres.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a498dc92f8819094a1108f8ac90f43 |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c33b6e108190b6b2bca4ddd97b6c |
completed | March 1, 2026, 10:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad08a9dd308190999d349f8b6297b8 |
completed | March 8, 2026, 5:27 a.m. |
Created at: March 1, 2026, 7:59 p.m.