Triple
T5916498
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Haute-Marne |
E131592
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Haute-Saône |
E136074
|
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: Haute-Saône | Statement: [Haute-Marne, borders, Haute-Saône]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Haute-Saône Context triple: [Haute-Marne, borders, Haute-Saône]
-
A.
Haute-Saône
chosen
Haute-Saône is a rural department in the Bourgogne-Franche-Comté region of eastern France, known for its forests, rivers, and historic villages.
-
B.
Haute-Marne
Haute-Marne is a rural department in northeastern France known for its forests, rivers, and historic towns such as Chaumont and Langres.
-
C.
Drôme
Drôme is a department in southeastern France known for its diverse landscapes, historic towns, and location between the Alps and the Rhône Valley.
-
D.
Jura department
The Jura department is an administrative region in eastern France known for its mountainous landscapes, forests, and lakes within the Jura Mountains.
-
E.
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.
- 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_69c0085a1ed08190a7e9a8b6323fd680 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c037bcea9c8190a34dc03857e3b80b |
completed | March 22, 2026, 6:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0e397f2748190acf2d629ee57a466 |
completed | March 23, 2026, 6:54 a.m. |
Created at: March 22, 2026, 3:59 p.m.