Triple
T215700
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Allier department |
E4815
|
entity |
| Predicate | hasINSEECode |
P7614
|
FINISHED |
| Object | 03 |
—
|
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: 03 | Statement: [Allier department, hasINSEECode, 03]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasINSEECode Context triple: [Allier department, hasINSEECode, 03]
-
A.
hasMunicipalityCode
Indicates that an entity is associated with a specific official municipality code used for administrative or identification purposes.
-
B.
hasINSEECODE
chosen
Indicates that an entity is associated with a specific INSEE code, identifying it within the French national statistical and administrative system.
-
C.
postalArea
Indicates that one entity is the postal or ZIP code area associated with the location or address represented by the other entity.
-
D.
NUTSRegionCode
Indicates the classification of an entity according to the NUTS (Nomenclature of Territorial Units for Statistics) regional coding system used for statistical regions.
-
E.
hasCanton
Indicates that an entity is administratively divided into, or associated with, a specific canton.
- 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_69a2575cb1dc8190a01ad332426dc339 |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25dcd2b208190855d5d8d70a3acfc |
completed | Feb. 28, 2026, 3:15 a.m. |
| PD | Predicate disambiguation | batch_69a25b52190481908f299d26122bafd2 |
completed | Feb. 28, 2026, 3:04 a.m. |
Created at: Feb. 28, 2026, 2:52 a.m.