Triple
T10798402
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Château-Salins |
E254769
|
entity |
| Predicate | departmentSeatType |
P47000
|
FINISHED |
| Object | subprefecture (historically) |
—
|
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: subprefecture (historically) | Statement: [Château-Salins, departmentSeatType, subprefecture (historically)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: departmentSeatType Context triple: [Château-Salins, departmentSeatType, subprefecture (historically)]
-
A.
hasDepartmentSeatRole
chosen
Indicates that an entity holds a specific role or position associated with a seat in a particular department.
-
B.
typicalSeat
Indicates the usual or standard seating position or location associated with an entity in a given context.
-
C.
seatCategory
Indicates the classification or type of a seat (e.g., by comfort level, price tier, or section) assigned to an entity.
-
D.
classesOfSeats
Indicates the different categories or types of seats associated with something, such as a venue, vehicle, or event.
-
E.
seatLocation
Indicates the spatial position or placement of a seat relative to a reference point or environment.
- 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_69d6aa61c15c8190a1839550c56e75e1 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d73334feb08190aae967eaa37659f7 |
completed | April 9, 2026, 5:03 a.m. |
| PD | Predicate disambiguation | batch_69d6f3188f00819094ee8d65b187a333 |
completed | April 9, 2026, 12:30 a.m. |
Created at: April 8, 2026, 9:17 p.m.