Triple
T333874
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yao |
E6680
|
entity |
| Predicate | hasUrbanType |
P749
|
FINISHED |
| Object | suburban city |
—
|
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: suburban city | Statement: [Yao, hasUrbanType, suburban city]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasUrbanType Context triple: [Yao, hasUrbanType, suburban city]
-
A.
hasUrbanFunction
Indicates that an entity serves a specific role or purpose within an urban context, such as providing services, infrastructure, or activities typical of a city environment.
-
B.
hasUrbanFeature
Indicates that a place or area possesses a specific urban element or infrastructure feature (such as roads, parks, or buildings) as part of its built environment.
-
C.
hasUrbanRole
Indicates that an entity plays a specific functional or social role within an urban or city context.
-
D.
urbanAreaType
chosen
Indicates the classification of an area based on its urban characteristics or development type (e.g., city, town, suburb, metropolitan region).
-
E.
isUrbanCounty
Indicates that a county is classified as urban, typically based on population density, development level, or similar urbanization criteria.
- 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_69a2e79434908190a9d5afe415153ad9 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2eac4d9d081908a624464e450fb0e |
completed | Feb. 28, 2026, 1:16 p.m. |
| PD | Predicate disambiguation | batch_69a2e94d99cc8190a112e4b630ec63c1 |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.