Triple
T22431471
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Augusto Severo International Airport |
E554509
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Parnamirim |
—
|
NE NERFINISHED |
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: Parnamirim | Statement: [Augusto Severo International Airport, locatedIn, Parnamirim]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Parnamirim Context triple: [Augusto Severo International Airport, locatedIn, Parnamirim]
-
A.
Parnamirim
chosen
Parnamirim is a rapidly growing city in northeastern Brazil known for its proximity to Natal and its historical role in World War II aviation.
-
B.
Pinheiral
Pinheiral is a small municipality in the state of Rio de Janeiro, Brazil, known for its rural character and growing role as a regional educational and residential center.
-
C.
Guarapari
Guarapari is a coastal resort city in southeastern Brazil known for its beaches and naturally radioactive monazite sand, which is popularly believed to have therapeutic properties.
-
D.
Araruama
Araruama is a coastal municipality in the state of Rio de Janeiro, Brazil, known for its large saltwater lagoon and tourism.
-
E.
Upanema
Upanema is a municipality in the state of Rio Grande do Norte in Brazil’s Northeast region.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e4f2d0c819091aa3558ea2ee630 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15a32139481909baaf9275f5e0257 |
completed | April 29, 2026, 1:09 a.m. |
Created at: April 16, 2026, 8:47 p.m.