Triple
T16473205
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saint-Prix |
E400117
|
entity |
| Predicate | regionSeatRelation |
P56411
|
FINISHED |
| Object | part of the Île-de-France administrative region |
—
|
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: part of the Île-de-France administrative region | Statement: [Saint-Prix, regionSeatRelation, part of the Île-de-France administrative region]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regionSeatRelation Context triple: [Saint-Prix, regionSeatRelation, part of the Île-de-France administrative region]
-
A.
regionSeatLocatedIn
chosen
Indicates that the administrative seat or capital of a region is located within that region.
-
B.
countySeatRegionRelation
Indicates that a region serves as the county seat (administrative center) for a given county.
-
C.
regionSeatsCount
Indicates the number of seats allocated or available within a specific region.
-
D.
regionOfAssociation
Indicates a broader geographic or spatial area with which an entity is functionally, contextually, or organizationally associated.
-
E.
hasCapitalRegionRelation
Indicates a relationship where one entity serves as the capital region or administrative capital area of another entity.
- 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_69d87f2dac988190b74d6e185fa88ba4 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e32dd266c48190991a1484eb2f7bcc |
completed | April 18, 2026, 7:08 a.m. |
| PD | Predicate disambiguation | batch_69e22706b0588190a48a951c5211a617 |
completed | April 17, 2026, 12:26 p.m. |
Created at: April 10, 2026, 5:11 a.m.