Triple
T28930178
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Feira de Santana |
E733758
|
entity |
| Predicate | populationRankInNortheastBrazil |
P180513
|
FINISHED |
| Object | among largest cities |
—
|
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: among largest cities | Statement: [Feira de Santana, populationRankInNortheastBrazil, among largest cities]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: populationRankInNortheastBrazil Context triple: [Feira de Santana, populationRankInNortheastBrazil, among largest cities]
-
A.
populationRankInBahia
chosen
Indicates the relative position of an entity in terms of population size compared to other entities within the state of Bahia.
-
B.
urbanAreaRankInBrazil
Indicates the relative position or ranking of an urban area compared to other urban areas within Brazil.
-
C.
significantPopulationInBrazilianState
Indicates that a population group or entity has a notably large or important presence within a specific Brazilian state.
-
D.
largestPopulationInBrazilianState
Indicates that the subject has the largest population among all entities within a specified Brazilian state.
-
E.
locatedInMesoregion
Indicates that an entity is geographically situated within a specific mesoregion.
- 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_69f05b0b49b08190b8994b339c7980f6 |
completed | April 28, 2026, 7 a.m. |
| NER | Named-entity recognition | batch_69f7465687bc8190a9da44d62b634ed7 |
completed | May 3, 2026, 12:57 p.m. |
| PD | Predicate disambiguation | batch_69f743f4ceb08190a21fe7f4a99b166b |
completed | May 3, 2026, 12:47 p.m. |
Created at: April 28, 2026, 8:27 a.m.