Triple
T13862418
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Srinagar International Airport |
E333228
|
entity |
| Predicate | hasParkingStand |
P67493
|
FINISHED |
| Object | aircraft stands for narrow-body jets |
—
|
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: aircraft stands for narrow-body jets | Statement: [Srinagar International Airport, hasParkingStand, aircraft stands for narrow-body jets]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasParkingStand Context triple: [Srinagar International Airport, hasParkingStand, aircraft stands for narrow-body jets]
-
A.
hasParkingFor
chosen
Indicates that a place or facility provides designated parking spaces suitable for a specified type of vehicle or user.
-
B.
hasParking
Indicates that a place or facility provides designated parking space(s) available for use.
-
C.
hasParkingNearby
Indicates that a location has one or more parking facilities or spaces available within a close surrounding area.
-
D.
hasTaxiStand
Indicates that a location or facility includes or is served by a designated taxi stand area where taxis can wait for passengers.
-
E.
hasParkStatus
Indicates that an entity holds a particular designation or status related to being a park (e.g., national park, city park, protected parkland).
- 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_69d81c5ced9c8190b0e9bcc6effe5959 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de23a101488190bd790b28033d38b9 |
completed | April 14, 2026, 11:23 a.m. |
| PD | Predicate disambiguation | batch_69de05972f3881909977b4c843984f88 |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 9, 2026, 10:14 p.m.