Triple
T16481427
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boutique Air |
E400326
|
entity |
| Predicate | typicalAircraftCapacity |
P36731
|
FINISHED |
| Object | 8 to 9 passengers |
—
|
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: 8 to 9 passengers | Statement: [Boutique Air, typicalAircraftCapacity, 8 to 9 passengers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalAircraftCapacity Context triple: [Boutique Air, typicalAircraftCapacity, 8 to 9 passengers]
-
A.
aircraftCapacity
chosen
Indicates the maximum number of passengers or amount of load that an aircraft is designed or allowed to carry.
-
B.
aircraftPanCapacity
Indicates the maximum number of passengers an aircraft is designed or allowed to carry.
-
C.
airWingCapacity
Indicates the maximum number or volume of aircraft or air operations that an air wing can support or handle.
-
D.
maximumPassengerCapacity
Indicates the greatest number of passengers that an entity is designed or allowed to carry at one time.
-
E.
planeNumber
Indicates that an entity is associated with a specific airplane identification number (such as a tail number or flight number).
- 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_69d883813098819084f5409539723b59 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e32e02b2c881909576a23bfbd3123c |
completed | April 18, 2026, 7:08 a.m. |
| PD | Predicate disambiguation | batch_69e22706b0588190a48a951c5211a617 |
completed | April 17, 2026, 12:26 p.m. |
Created at: April 10, 2026, 5:13 a.m.