Triple
T9749481
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Berrichonne de Châteauroux |
E236402
|
entity |
| Predicate | homeCity |
P263
|
FINISHED |
| Object | Châteauroux |
E43984
|
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: Châteauroux | Statement: [La Berrichonne de Châteauroux, homeCity, Châteauroux]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Châteauroux Context triple: [La Berrichonne de Châteauroux, homeCity, Châteauroux]
-
A.
Châteauroux
chosen
Châteauroux is a city in central France that will host the shooting events for the 2024 Summer Olympics.
-
B.
Chapeauroux
Chapeauroux is a river in central France that flows through the Massif Central before joining the Allier.
-
C.
Châtellerault
Châtellerault is a historic town in western France, known for its former royal arms factory and its role as an important industrial and transport hub in the Vienne department.
-
D.
La Châtre
La Châtre is a small historic town in central France known for its picturesque medieval streets and its association with the writer George Sand.
-
E.
Montluçon
Montluçon is a historic industrial town in central France known for its medieval old quarter and role as a key urban center in the Allier department.
- 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_69ca84d4eddc8190996fec1417d2bae8 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9f6a2f8c8190a6f6af6587ee90b8 |
completed | April 1, 2026, 10:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d96af89e48819093a6c149ac988391 |
completed | April 10, 2026, 9:26 p.m. |
Created at: March 30, 2026, 8:23 p.m.