Triple
T13389939
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coutances |
E319547
|
entity |
| Predicate | hasDemonym |
P191
|
FINISHED |
| Object | Coutançaise |
E1038535
|
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: Coutançaise | Statement: [Coutances, hasDemonym, Coutançaise]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Coutançaise Context triple: [Coutances, hasDemonym, Coutançaise]
-
A.
Coutançais
chosen
Coutançais is the French demonym for inhabitants of the town of Coutances in Normandy.
-
B.
Cottévrard
Cottévrard is a small commune in the Seine-Maritime department of the Normandy region in northern France.
-
C.
Nançon River
The Nançon River is a small watercourse in northwestern France that flows through the historic town of Fougères in the Ille-et-Vilaine department of Brittany.
-
D.
Thiérache
Thiérache is a rural, historically fortified region in northern France known for its bocage landscapes, brick churches, and traditional dairy production.
-
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_69d806b886bc8190b676e7768b8e01c5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dba0d543348190a9c1be509b015c0f |
completed | April 12, 2026, 1:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f73981816881908aac3ab6b1921904 |
completed | May 3, 2026, 12:03 p.m. |
Created at: April 9, 2026, 9:34 p.m.