Triple
T1622755
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ES Troyes AC |
E35069
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Aube |
E43606
|
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: Aube | Statement: [ES Troyes AC, locatedIn, Aube]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aube Context triple: [ES Troyes AC, locatedIn, Aube]
-
A.
Aube
chosen
Aube is a department in northeastern France known for its historic towns, Champagne vineyards, and rural landscapes.
-
B.
Val-d'Oise
Val-d'Oise is a department in northern France that forms part of the Paris metropolitan region and includes both suburban areas and rural landscapes.
-
C.
Oise
Oise is a major river in northern France that flows through regions such as Picardy and Île-de-France before joining the Seine near Paris.
-
D.
Aisne
Aisne is a department in northern France known for its historic towns, World War I battlefields, and rural landscapes.
-
E.
Creuse
Creuse is a rural department in central France known for its sparsely populated landscapes, traditional agriculture, and part of the historic Limousin region.
- 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_69a886023194819080a3fccd6e325d0e |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a909cf3c7481909ddbe6a6596bb0c8 |
completed | March 5, 2026, 4:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad58ccc80c819088ecd91f0a99a247 |
completed | March 8, 2026, 11:09 a.m. |
Created at: March 4, 2026, 7:28 p.m.