Triple
T28772542
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris (department) |
E726449
|
entity |
| Predicate | isBothMunicipalityAndDepartment |
P165570
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Paris (department), isBothMunicipalityAndDepartment, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isBothMunicipalityAndDepartment Context triple: [Paris (department), isBothMunicipalityAndDepartment, true]
-
A.
isMunicipalFormationOf
Indicates that one administrative unit is formally established and recognized as the municipal formation corresponding to another territorial or administrative entity.
-
B.
isPortMunicipality
Indicates that a municipality functions as a port, having facilities for maritime transport and related activities.
-
C.
isMunicipal
Indicates that something belongs to, is administered by, or is characteristic of a municipality or local city government.
-
D.
isPartOfMunicipalityType
Indicates that one administrative unit or area belongs to, or is classified under, a specific type or category of municipality.
-
E.
hasMunicipalPart
Indicates that an administrative or territorial entity includes a municipality as one of its constituent parts.
- F. None of above. chosen
Provenance (4 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_69f03199997c8190b6ae43fb19312443 |
completed | April 28, 2026, 4:03 a.m. |
| NER | Named-entity recognition | batch_69f6596b210481908af6cd555748f75b |
completed | May 2, 2026, 8:07 p.m. |
| PD | Predicate disambiguation | batch_69f65760fd3081908ffe014a5e2bf069 |
completed | May 2, 2026, 7:58 p.m. |
| PDg | Predicate description generation | batch_69f658ebeca4819096beb3f98f73fe31 |
completed | May 2, 2026, 8:05 p.m. |
Created at: April 28, 2026, 6:16 a.m.