Triple
T5987499
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Montebelluna |
E133263
|
entity |
| Predicate | twinTown |
P1072
|
FINISHED |
| Object | Gagny |
E28558
|
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: Gagny | Statement: [Montebelluna, twinTown, Gagny]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gagny Context triple: [Montebelluna, twinTown, Gagny]
-
A.
Gagny
chosen
Gagny is a suburban commune in the eastern outskirts of Paris, France, known primarily as a residential town within the Seine-Saint-Denis department.
-
B.
Givry
Givry is a Burgundy wine appellation in eastern France, noted for its predominantly Pinot Noir red wines with a reputation for good value and quality.
-
C.
Cugny
Cugny is a locality within the municipality of Bernex in the canton of Geneva, Switzerland.
-
D.
Santenay
Santenay is a wine-producing village in Burgundy, France, known for its predominantly red wines made from Pinot Noir and its location at the southern end of the Côte de Beaune.
-
E.
Verzenay
Verzenay is a renowned Grand Cru wine-producing village in France’s Champagne region, particularly noted for its Pinot Noir vineyards on the Montagne de Reims.
- 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_69c0087010d081908bb8142342d63330 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c04dc38a308190a368c5c787a5fc64 |
completed | March 22, 2026, 8:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c1135e653481909869094063d31605 |
completed | March 23, 2026, 10:18 a.m. |
Created at: March 22, 2026, 4:04 p.m.