Triple
T30441757
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | アメリカ合衆国ミシガン州リボニア市 |
E774463
|
entity |
| Predicate | 属する都市圏 |
P108274
|
FINISHED |
| Object | デトロイト・ウォーレン・ディアボーン都市圏 |
—
|
NE NERFINISHED |
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: デトロイト・ウォーレン・ディアボーン都市圏 | Statement: [アメリカ合衆国ミシガン州リボニア市, 属する都市圏, デトロイト・ウォーレン・ディアボーン都市圏]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 属する都市圏 Context triple: [アメリカ合衆国ミシガン州リボニア市, 属する都市圏, デトロイト・ウォーレン・ディアボーン都市圏]
-
A.
belongsToMetropolitanRegion
chosen
Indicates that one geographic or administrative area is part of, or included within, a larger metropolitan region.
-
B.
partOfMetropolitanArea
Indicates that one place is included within and belongs to the larger metropolitan area of another place.
-
C.
containsSuburbanAreaOf
Indicates that one geographic region includes within its boundaries a suburban area belonging to or associated with another region.
-
D.
所在地エリア
Indicates the geographical area or region in which an entity is located or based.
-
E.
relatedUrbanArea
Indicates that one urban area is geographically or functionally associated with another urban area, such as being nearby, connected, or part of the same broader metropolitan context.
- 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_69f22493ef9c8190ae8c2afcb7f994c8 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f6869948e481908901dbda23952cc0 |
completed | May 2, 2026, 11:19 p.m. |
| PD | Predicate disambiguation | batch_69f678d2196c8190b9d0d2fcd47cc539 |
completed | May 2, 2026, 10:21 p.m. |
Created at: April 29, 2026, 8:08 p.m.