Triple
T36270589
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Xian MA60 |
E892659
|
entity |
| Predicate | numberOfCrewInCockpit |
—
|
GENERATED |
| Object | 2 |
—
|
UNRECOGNIZED GENERATED |
How this triple was built (1 step)
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.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfCrewInCockpit Context triple: [Xian MA60, numberOfCrewInCockpit, 2]
-
A.
totalCrewMembers
Indicates the total number of crew members associated with a given entity or context.
-
B.
crewCount
Indicates the number of crew members associated with an entity, such as a vehicle, vessel, or mission.
-
C.
aircrewCapacity
Indicates the maximum number of aircrew members that a vehicle or aircraft is designed or allowed to carry.
-
D.
crewAndPassengersCount
Indicates the total number of people on a vehicle or vessel, combining both crew members and passengers.
-
E.
crewCountApproximate
Indicates that the relationship specifies an estimated or approximate number of crew members associated with an entity.
- F. None of above. chosen
Provenance (1 batch)
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_69f76e488f34819083e254dbe288c27a |
completed | May 3, 2026, 3:48 p.m. |
Created at: May 3, 2026, 4:09 p.m.