Triple
T1127629
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charlotte Douglas International Airport |
E24755
|
entity |
| Predicate | isBusiestAirportRankingInWorldByAircraftMovements |
P20776
|
FINISHED |
| Object | among top 10 |
—
|
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: among top 10 | Statement: [Charlotte Douglas International Airport, isBusiestAirportRankingInWorldByAircraftMovements, among top 10]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isBusiestAirportRankingInWorldByAircraftMovements Context triple: [Charlotte Douglas International Airport, isBusiestAirportRankingInWorldByAircraftMovements, among top 10]
-
A.
rankingByAircraftMovements
chosen
Indicates the relative order of entities based on the number of aircraft movements (takeoffs and landings) they handle.
-
B.
passengerTrafficRankingWorld
Indicates the relative position of an entity in a global ranking based on the volume of passenger traffic it handles.
-
C.
airportRank
Indicates the relative position or level assigned to an airport within a ranking or ordered list.
-
D.
oneOfBusiestAirportsIn
Indicates that an airport is among the busiest airports within a specified location or region.
-
E.
airportRankInFranceByTraffic
Indicates the relative position of an airport in France when airports are ordered by the volume of passenger or cargo traffic they handle.
- 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_69a4940712c88190aa244f3fc6070a65 |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bc4bc21881909dcfe628f59f3e8c |
completed | March 1, 2026, 10:23 p.m. |
| PD | Predicate disambiguation | batch_69a4bb48de2081909a0dce005b1c9df1 |
completed | March 1, 2026, 10:18 p.m. |
Created at: March 1, 2026, 7:44 p.m.