Triple
T17766576
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Loire |
E443522
|
entity |
| Predicate | bordersDepartment |
P224
|
FINISHED |
| Object | Allier |
—
|
NE NERFINISHED |
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: Allier | Statement: [Loire, bordersDepartment, Allier]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Allier Context triple: [Loire, bordersDepartment, Allier]
-
A.
Allier
chosen
Allier is a major river in central France that flows northward through the Massif Central before joining the Loire.
-
B.
Arpitanie
Arpitanie is a cultural and linguistic region in parts of France, Switzerland, and Italy where the Arpitan (Franco-Provençal) language and related traditions are historically rooted.
-
C.
L’Union
L’Union is a French regional newspaper known for covering local and national news in the Champagne-Ardenne and surrounding areas.
-
D.
L’Union
L’Union is a suburban commune in southwestern France, located just northeast of Toulouse and integrated into its metropolitan area.
-
E.
Franca
Franca is a city in the northeastern part of the Brazilian state of São Paulo, known historically for its leather and footwear industry.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9edf16c8190a59ebd245d378f4f |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e485fccb9881908923564bf319f3c1 |
completed | April 19, 2026, 7:36 a.m. |
Created at: April 10, 2026, 10:11 a.m.