Triple
T30441769
| 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 | 都市形態 |
P67280
|
FINISHED |
| Object | 自動車交通を前提としたスプロール型市街地 |
—
|
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: 自動車交通を前提としたスプロール型市街地 | Statement: [アメリカ合衆国ミシガン州リボニア市, 都市形態, 自動車交通を前提としたスプロール型市街地]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 都市形態 Context triple: [アメリカ合衆国ミシガン州リボニア市, 都市形態, 自動車交通を前提としたスプロール型市街地]
-
A.
都市的性格
Indicates the characteristic qualities or distinctive personality that define a city’s overall atmosphere or identity.
-
B.
通過都市
Indicates passing through or going via a city as part of a route or movement.
-
C.
isUrbanForm
chosen
Indicates that an entity represents or exhibits characteristics of an urban built environment or city-like spatial structure.
-
D.
urbanLandscapeCharacterizedBy
Indicates that an urban landscape possesses or is defined by specific distinguishing features, qualities, or elements.
-
E.
containsUrbanForm
Indicates that one entity spatially includes or encompasses an urban form or built-up area within its extent.
- 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.