Triple
T19265493
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | arrondissement of Verdun |
E481764
|
entity |
| Predicate | hasMunicipalSubdivision |
P747
|
FINISHED |
| Object | commune |
—
|
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: commune | Statement: [arrondissement of Verdun, hasMunicipalSubdivision, commune]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMunicipalSubdivision Context triple: [arrondissement of Verdun, hasMunicipalSubdivision, commune]
-
A.
hasMunicipalPart
Indicates that an administrative or territorial entity includes a municipality as one of its constituent parts.
-
B.
hasSubdivision
chosen
Indicates that one entity is divided into and contains another entity as one of its constituent parts or administrative units.
-
C.
hasMunicipalDistrict
Indicates that an administrative entity includes or is divided into one or more municipal districts as its subordinate units.
-
D.
hasSuburbanMunicipality
Indicates that one administrative region includes or is associated with a municipality located in a suburban area.
-
E.
hasMunicipalAgglomeration
Indicates that one administrative or geographic entity is part of, or associated with, a larger municipal agglomeration encompassing multiple urban or suburban areas.
- 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_69d8e8ce54cc8190998418ff1f66ef28 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5fb8ca2e88190baad3b6c199ee036 |
completed | April 20, 2026, 10:10 a.m. |
| PD | Predicate disambiguation | batch_69e4dd07a7208190afcd51ba1dc87c33 |
completed | April 19, 2026, 1:47 p.m. |
Created at: April 10, 2026, 1:29 p.m.