Triple
T4723543
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chambon-sur-Voueize |
E104825
|
entity |
| Predicate | INSEEcode |
P27964
|
FINISHED |
| Object | 23045 |
—
|
LITERAL 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: 23045 | Statement: [Chambon-sur-Voueize, INSEEcode, 23045]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: INSEEcode Context triple: [Chambon-sur-Voueize, INSEEcode, 23045]
-
A.
inseeCode
chosen
Indicates the official INSEE (French national statistics institute) code assigned to an entity, typically identifying a specific geographic or administrative unit.
-
B.
formerINSEECode
Indicates that an entity was previously identified by a different INSEE code before being assigned its current one.
-
C.
hasCommune
Indicates a relationship where an entity is associated with, belongs to, or is located within a specific commune (municipal administrative unit).
-
D.
cantonCode
Indicates the specific administrative canton identifier associated with an entity or location.
-
E.
hasArrondissement
Indicates a relationship where an administrative unit or locality is associated with, or belongs to, a specific arrondissement.
- F. None of above.
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_69bd43ed84648190ae0b7ee8e8d00482 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd67c9c3c08190a6c4944cdd1362a8 |
completed | March 20, 2026, 3:29 p.m. |
| PD | Predicate disambiguation | batch_69bd6220071881909670c89d072ffb6d |
completed | March 20, 2026, 3:05 p.m. |
Created at: March 20, 2026, 1:18 p.m.