Triple
T24865945
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kumasi Airport |
E622281
|
entity |
| Predicate | isSecondBusiestAirportIn |
P89625
|
FINISHED |
| Object | Ghana |
—
|
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: Ghana | Statement: [Kumasi Airport, isSecondBusiestAirportIn, Ghana]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isSecondBusiestAirportIn Context triple: [Kumasi Airport, isSecondBusiestAirportIn, Ghana]
-
A.
isSecondLargestAirportIn
chosen
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.
-
B.
hasSecondaryAirport
Indicates that an entity is associated with an additional, typically smaller or alternative, airport beyond its primary one.
-
C.
busiestAirportIn
Indicates that the subject location contains or is associated with the airport that has the highest level of traffic or activity within that location.
-
D.
oneOfBusiestAirportsIn
Indicates that an airport is among the busiest airports within a specified location or region.
-
E.
otherMainAirportInCountry
Indicates that one airport is another primary airport located within the same country as the first.
- 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_69e2fac350d08190b3affde1b451a8c5 |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f43043512481909501a3979cac9947 |
completed | May 1, 2026, 4:46 a.m. |
| PD | Predicate disambiguation | batch_69f420fd375c81908ea4a4e60b76ee8f |
completed | May 1, 2026, 3:41 a.m. |
Created at: April 18, 2026, 5:22 a.m.