Triple
T22210418
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Asaba International Airport |
E548928
|
entity |
| Predicate | servesFlightScope |
P147303
|
FINISHED |
| Object | regional |
—
|
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: regional | Statement: [Asaba International Airport, servesFlightScope, regional]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: servesFlightScope Context triple: [Asaba International Airport, servesFlightScope, regional]
-
A.
flightScope
Indicates a relationship where the extent, range, or coverage of a flight (such as its route, duration, or operational limits) is specified or constrained.
-
B.
servesSport
Indicates that one entity provides or is used for playing, supporting, or accommodating a particular sport.
-
C.
caddieServices
Indicates that one entity serves as a caddie, providing support and assistance (such as carrying equipment and offering guidance) to another entity, typically in a golf context.
-
D.
servesFeature
Indicates that one entity provides or makes available a particular feature or functionality to another entity.
-
E.
golfCourseUse
Indicates that an entity is used as a golf course or for playing golf.
- F. None of above. chosen
Provenance (4 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_69e11e3f7e04819089806d81d5ac431e |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12b2bcf748190a9721f0c9ae17e70 |
completed | April 28, 2026, 9:48 p.m. |
| PD | Predicate disambiguation | batch_69e71b4dcc408190a30429fb08fcf39e |
completed | April 21, 2026, 6:38 a.m. |
| PDg | Predicate description generation | batch_69e723f65c5c8190a0ee3c539e5d0767 |
completed | April 21, 2026, 7:15 a.m. |
Created at: April 16, 2026, 8:36 p.m.