Triple
T34359885
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | São Caetano do Sul |
E881842
|
entity |
| Predicate | highlyUrbanized |
P135657
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [São Caetano do Sul, highlyUrbanized, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: highlyUrbanized Context triple: [São Caetano do Sul, highlyUrbanized, true]
-
A.
isHighlyUrbanizedCity
chosen
Indicates that a city has a very high level of urban development, density, and built-up infrastructure relative to typical cities.
-
B.
isHighlyUrbanizedCityOf
Indicates that a city is characterized by a high degree of urban development and population density within the specified larger region or jurisdiction.
-
C.
isUrbanized
Indicates that a place or area has been developed with dense human settlement, infrastructure, and built environment characteristic of a city or town.
-
D.
isInHighlyUrbanizedCity
Indicates that the subject is located within a city characterized by a high degree of urban development, density, and infrastructure.
-
E.
hasHigherUrbanizationThan
Indicates that one entity has a greater proportion of its population living in urban areas compared to 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_69f349be5c9c81908dc726ae1f4c68f2 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f71c35327c8190884f1bfe12bd2cd7 |
completed | May 3, 2026, 9:58 a.m. |
| PD | Predicate disambiguation | batch_69f71822d0e88190ac9731c7ae5a4def |
completed | May 3, 2026, 9:40 a.m. |
Created at: May 1, 2026, 1:58 a.m.