Triple
T18316793
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Londrina |
E438769
|
entity |
| Predicate | populationRankInParaná |
P91471
|
FINISHED |
| Object | second largest city in Paraná |
—
|
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: second largest city in Paraná | Statement: [Londrina, populationRankInParaná, second largest city in Paraná]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: populationRankInParaná Context triple: [Londrina, populationRankInParaná, second largest city in Paraná]
-
A.
urbanAreaRankInBrazil
Indicates the relative position or ranking of an urban area compared to other urban areas within Brazil.
-
B.
hasPopulationRankInChile
Indicates the relative position of an entity in the ordered ranking of populations within Chile.
-
C.
populationRankInBolivia
Indicates the relative position of an entity in terms of population size compared to other entities within Bolivia.
-
D.
significantPopulationInBrazilianState
chosen
Indicates that a population group or entity has a notably large or important presence within a specific Brazilian state.
-
E.
rankByAreaInChile
Indicates the relative ordering of entities based on their area size within the geographic boundaries of Chile.
- 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_69d8b916a2d081909e249e4902f6aad9 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e5021e61008190a300b6c51976a837 |
completed | April 19, 2026, 4:26 p.m. |
| PD | Predicate disambiguation | batch_69e44fe4ee10819086b4142444fca1f5 |
completed | April 19, 2026, 3:45 a.m. |
Created at: April 10, 2026, 10:36 a.m.