Triple
T4813143
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fortaleza |
E107118
|
entity |
| Predicate | urbanAreaRankInBrazil |
P59783
|
FINISHED |
| Object | one of the largest metropolitan areas in Brazil |
—
|
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: one of the largest metropolitan areas in Brazil | Statement: [Fortaleza, urbanAreaRankInBrazil, one of the largest metropolitan areas in Brazil]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: urbanAreaRankInBrazil Context triple: [Fortaleza, urbanAreaRankInBrazil, one of the largest metropolitan areas in Brazil]
-
A.
IBGECode
Indicates the official numerical code assigned to a Brazilian geographic entity by the IBGE (Brazilian Institute of Geography and Statistics).
-
B.
metroArea
Indicates that one location is part of, or belongs to, a specified metropolitan area.
-
C.
areaRank
Indicates the relative ordering or position of an entity based on the size of its area compared to others.
-
D.
distanceToSãoPaulo
Indicates the spatial distance between a given entity’s location and the city of São Paulo.
-
E.
populationRankInPortugal
Indicates the relative position of an entity in terms of population size compared to other entities within Portugal.
- F. None of above. chosen
Provenance (4 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_69bd43f779448190b92885cb70abb6c2 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6ddd17d881909f7731ff2b460e83 |
completed | March 20, 2026, 3:55 p.m. |
| PD | Predicate disambiguation | batch_69bd6c1dfa3481909d240d50ed0ee38c |
completed | March 20, 2026, 3:47 p.m. |
| PDg | Predicate description generation | batch_69bd6dda5e808190a26ec85e4499d8e4 |
completed | March 20, 2026, 3:55 p.m. |
Created at: March 20, 2026, 1:23 p.m.