Triple
T22642680
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Estádio do Arruda |
E558871
|
entity |
| Predicate | countryRankByCapacity |
P31652
|
FINISHED |
| Object | one of the largest stadiums 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 stadiums in Brazil | Statement: [Estádio do Arruda, countryRankByCapacity, one of the largest stadiums in Brazil]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: countryRankByCapacity Context triple: [Estádio do Arruda, countryRankByCapacity, one of the largest stadiums in Brazil]
-
A.
capacityRankInWorld
Indicates the relative position or ranking of an entity’s capacity compared to all similar entities worldwide.
-
B.
rankByCapacityInUS
Indicates the relative ordering of entities based on their capacity within the United States.
-
C.
regionRankBySize
Indicates the relative ordering of regions based on their physical size, from largest to smallest (or vice versa).
-
D.
rankByCapacityInAfrica
Indicates the relative ordering of entities based on their capacity within the context of Africa.
-
E.
rankingInCountryBySize
chosen
Indicates the position of an entity in an ordered list of entities within a specific country, based on their relative size.
- 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_69e24547f7fc819086e2c4ba3b979657 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1703489a48190bd97a7eb7a571b64 |
completed | April 29, 2026, 2:43 a.m. |
| PD | Predicate disambiguation | batch_69ee6294c4c08190b7e4829f4b9af24b |
completed | April 26, 2026, 7:08 p.m. |
Created at: April 17, 2026, 3:04 p.m.