Triple
T2553999
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jinnah International Airport |
E56690
|
entity |
| Predicate | hasParkingApron |
P40806
|
FINISHED |
| Object | commercial aircraft |
—
|
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: commercial aircraft | Statement: [Jinnah International Airport, hasParkingApron, commercial aircraft]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasParkingApron Context triple: [Jinnah International Airport, hasParkingApron, commercial aircraft]
-
A.
hasParking
Indicates that a place or facility provides designated parking space(s) available for use.
-
B.
hasHeliport
Indicates that an entity possesses or is equipped with a heliport facility for helicopter landing and takeoff.
-
C.
hasHelipads
Indicates that an entity possesses one or more helipads available for helicopter landing and takeoff.
-
D.
hasStandingArea
Indicates that an entity includes or provides a designated area where people can stand.
-
E.
hasRunwayMarkings
Indicates that a runway possesses specific painted markings or symbols on its surface.
- 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_69ab4a4bfec081908039988ec4c86e28 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd5a33234819082ad49fa6594b6be |
completed | March 7, 2026, 7:37 a.m. |
| PD | Predicate disambiguation | batch_69abd0c8b6f08190a68645db3e8b779a |
completed | March 7, 2026, 7:16 a.m. |
| PDg | Predicate description generation | batch_69abd5a1cd508190a660b9a3c6b7cbcb |
completed | March 7, 2026, 7:37 a.m. |
Created at: March 6, 2026, 9:48 p.m.