Triple
T25447450
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nairobi–London |
E637674
|
entity |
| Predicate | typicalDestinationAirportCode |
P45615
|
FINISHED |
| Object | LHR |
—
|
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: LHR | Statement: [Nairobi–London, typicalDestinationAirportCode, LHR]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalDestinationAirportCode Context triple: [Nairobi–London, typicalDestinationAirportCode, LHR]
-
A.
typicalDestinationAirportIATA
chosen
Indicates the IATA airport code that is typically the destination in this kind of trip or route.
-
B.
typicalOriginAirportIATA
Indicates the usual or primary origin airport for an entity, identified by its IATA airport code.
-
C.
destinationAirportICAO
Indicates the airport, identified by its ICAO code, that serves as the destination in a flight or travel-related context.
-
D.
destinationAirportLocation
Indicates the geographic location associated with the airport that serves as the destination in a trip or flight.
-
E.
hasTypicalOriginAirport
Indicates that an entity, such as a flight route or airline service, is commonly or usually associated with a particular origin airport from which it typically departs.
- 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_69e75db7c5048190b8da9cd7eeedb610 |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69fd3d46d1f48190a1b20dd063224b7d |
completed | May 8, 2026, 1:32 a.m. |
| PD | Predicate disambiguation | batch_69fd3ae1510c81908fe1280efc17feee |
completed | May 8, 2026, 1:22 a.m. |
Created at: April 21, 2026, 2:02 p.m.