Triple
T215697
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Allier department |
E4815
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | Vichy |
E1394
|
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: Vichy | Statement: [Allier department, containsCity, Vichy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vichy Context triple: [Allier department, containsCity, Vichy]
-
A.
Vichy
chosen
Vichy is a spa town in central France renowned for its thermal springs, health resorts, and role as the seat of the World War II Vichy regime.
-
B.
Limoges
Limoges is a historic city in central France renowned for its fine porcelain production and medieval architecture.
-
C.
Saint-Cloud
Saint-Cloud is a commune just west of Paris, historically notable as the site where Napoleon Bonaparte staged the Coup of 18 Brumaire that ended the French Directory and ushered in his rule.
-
D.
Reims
Reims is a historic city in northeastern France known for its Gothic cathedral, role in French coronations, and significance during both World Wars.
-
E.
Clermont-Ferrand
Clermont-Ferrand is a central French city known for its historic cathedral built of black volcanic stone and as the longtime headquarters of the tire company Michelin.
- 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_69a2575cb1dc8190a01ad332426dc339 |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25c4ca0c8819093f63c6371e2d140 |
completed | Feb. 28, 2026, 3:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a4746825348190892c8f6341796dac |
completed | March 1, 2026, 5:16 p.m. |
Created at: Feb. 28, 2026, 2:52 a.m.