Triple
T21426344
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Forez |
E528566
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Roannais |
—
|
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: Roannais | Statement: [Forez, borders, Roannais]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Roannais Context triple: [Forez, borders, Roannais]
-
A.
Roannais
chosen
Roannais is a natural region in central France known for its rolling countryside, agricultural landscapes, and proximity to the upper Loire River.
-
B.
Brionnais
Brionnais is a historic rural region in eastern France known for its Romanesque churches, traditional stone villages, and Charolais cattle farming.
-
C.
Creillois
Creillois is the French term for inhabitants of the town of Creil in northern France.
-
D.
Rouans
Rouans is a commune in western France’s Loire-Atlantique department, known for its rural character and proximity to the Loire estuary.
-
E.
Dagneux
Dagneux is a commune in eastern France’s Ain department, known for its residential character and proximity to the Lyon metropolitan area.
- 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_69e0c455f3688190810bc96365791b0f |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ee813c7a048190a400e364c8df1dcf |
completed | April 26, 2026, 9:18 p.m. |
Created at: April 16, 2026, 5:48 p.m.