Triple
T4439652
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Aurora International Airport |
E95738
|
entity |
| Predicate | relativeTrafficLevel |
P56035
|
FINISHED |
| Object | one of the busiest airports in Central America |
—
|
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 busiest airports in Central America | Statement: [La Aurora International Airport, relativeTrafficLevel, one of the busiest airports in Central America]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relativeTrafficLevel Context triple: [La Aurora International Airport, relativeTrafficLevel, one of the busiest airports in Central America]
-
A.
trafficLevel
Indicates the degree of congestion or flow intensity present in a transportation network or route at a given time.
-
B.
trafficDirection
Indicates the direction in which traffic is intended or allowed to move relative to a given reference point or segment.
-
C.
cargoTrafficRank
Indicates the relative position of an entity in an ordered list based on the volume or intensity of its cargo traffic.
-
D.
roadTraffic
Indicates the presence, flow, or conditions of vehicles and movement along roads or streets.
-
E.
annualTraffic
Indicates the typical amount or volume of traffic associated with something over the course of a year.
- 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_69b3453ea2b48190a26f154b3b8fece5 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b355aaa9288190b95d875d343d6ee5 |
completed | March 13, 2026, 12:09 a.m. |
| PD | Predicate disambiguation | batch_69b34f6078cc8190831b89f404198cc5 |
completed | March 12, 2026, 11:42 p.m. |
| PDg | Predicate description generation | batch_69b3505a87b4819083fbbd58870e520b |
completed | March 12, 2026, 11:46 p.m. |
Created at: March 12, 2026, 11:31 p.m.