Triple
T34359896
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | São Caetano do Sul |
E881842
|
entity |
| Predicate | urbanizationRate |
P159517
|
FINISHED |
| Object | near 100 percent |
—
|
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: near 100 percent | Statement: [São Caetano do Sul, urbanizationRate, near 100 percent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: urbanizationRate Context triple: [São Caetano do Sul, urbanizationRate, near 100 percent]
-
A.
urbanizationLevel
Indicates the degree to which an area or population is characterized by urban development, infrastructure, and density of human settlement.
-
B.
hasHighestUrbanizationRateIn
Indicates that the subject has the greatest proportion of its population living in urban areas compared to all other entities within the specified object region or group.
-
C.
hasHigherUrbanizationThan
Indicates that one entity has a greater proportion of its population living in urban areas compared to another entity.
-
D.
hasUrbanPopulationShare
chosen
Indicates the proportion of a population that resides in urban areas relative to the total population.
-
E.
hasUrbanPopulationIn
Indicates that an entity has a specified urban population within a particular geographic area or administrative unit.
- 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_69f727bde8f88190ad746ca515134ca1 |
completed | May 3, 2026, 10:47 a.m. |
| PD | Predicate disambiguation | batch_69f72739c30c81908642eef3feb3afcf |
completed | May 3, 2026, 10:45 a.m. |
Created at: May 1, 2026, 1:58 a.m.