Triple
T29906663
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jardim Oceânico |
E759554
|
entity |
| Predicate | nearestSecondaryAirport |
P40597
|
FINISHED |
| Object | Santos Dumont Airport |
—
|
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: Santos Dumont Airport | Statement: [Jardim Oceânico, nearestSecondaryAirport, Santos Dumont Airport]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nearestSecondaryAirport Context triple: [Jardim Oceânico, nearestSecondaryAirport, Santos Dumont Airport]
-
A.
hasSecondaryAirport
chosen
Indicates that an entity is associated with an additional, typically smaller or alternative, airport beyond its primary one.
-
B.
otherMainAirportInCountry
Indicates that one airport is another primary airport located within the same country as the first.
-
C.
previousPrimaryAirportFor
Indicates that one airport was formerly the main or primary airport serving a particular location or entity before being replaced by another.
-
D.
isSecondLargestAirportIn
Indicates that an airport is the second largest (by a specified measure, such as passenger traffic or area) among all airports within a given region or jurisdiction.
-
E.
otherAirportOfCity
Indicates that the subject airport is another airport serving the same city as the object airport.
- 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_69f224600590819085e148a01c056ef6 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f6cee45590819086e489bfccbe4ac3 |
completed | May 3, 2026, 4:28 a.m. |
| PD | Predicate disambiguation | batch_69f6cc1188708190b8f0f56e595e6057 |
completed | May 3, 2026, 4:16 a.m. |
Created at: April 29, 2026, 6:08 p.m.