Triple
T33696075
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MDE |
E863315
|
entity |
| Predicate | associatedAirportLocatedInCountry |
—
|
GENERATED |
| Object | Colombia |
—
|
UNRECOGNIZED GENERATED |
How this triple was built (1 step)
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.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedAirportLocatedInCountry Context triple: [MDE, associatedAirportLocatedInCountry, Colombia]
-
A.
associatedAirport
Indicates a relationship where an entity is linked or connected to a specific airport, typically as its relevant or corresponding airport.
-
B.
servesCountryThroughAirport
Indicates that an airline or service provider operates flights or services to a country via a specific airport.
-
C.
associatedAirportFocusCityFor
Indicates that an airport serves as a designated focus city for a particular airline or carrier.
-
D.
associatedWithAirportName
Indicates a relationship where an entity is linked or connected to a specific airport by its name.
-
E.
airportLocationCountry
chosen
Indicates that an airport is geographically situated within a specific country.
- F. None of above.
Provenance (1 batch)
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_69f3498723a08190ac034339cc78eade |
completed | April 30, 2026, 12:22 p.m. |
Created at: May 1, 2026, 1:43 a.m.