Triple
T24183062
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kisangani Simisini Airport |
E599476
|
entity |
| Predicate | hasAirportServingSameCity |
P31283
|
FINISHED |
| Object | Bangoka International Airport |
—
|
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: Bangoka International Airport | Statement: [Kisangani Simisini Airport, hasAirportServingSameCity, Bangoka International Airport]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAirportServingSameCity Context triple: [Kisangani Simisini Airport, hasAirportServingSameCity, Bangoka International Airport]
-
A.
servedByAirportInOriginCity
Indicates that the origin city of a trip or route is served by a particular airport.
-
B.
hasSecondaryAirport
Indicates that an entity is associated with an additional, typically smaller or alternative, airport beyond its primary one.
-
C.
isLocatedAtAirportServingCity
Indicates that something is situated at an airport that provides service to a particular city.
-
D.
otherAirportOfCity
chosen
Indicates that the subject airport is another airport serving the same city as the object airport.
-
E.
associatedAirport
Indicates a relationship where an entity is linked or connected to a specific airport, typically as its relevant or corresponding airport.
- 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_69e288cca05481908faeb1563711114a |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f27c9ddfcc819096697a844b300cce |
completed | April 29, 2026, 9:48 p.m. |
| PD | Predicate disambiguation | batch_69f1c42f942c8190b103ff29a60fef34 |
completed | April 29, 2026, 8:41 a.m. |
Created at: April 17, 2026, 11:34 p.m.