Triple
T1093626
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boeing 787 Dreamliner |
E24221
|
entity |
| Predicate | numberOfCrew |
P8587
|
FINISHED |
| Object | 2 (flight deck) |
—
|
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: 2 (flight deck) | Statement: [Boeing 787 Dreamliner, numberOfCrew, 2 (flight deck)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfCrew Context triple: [Boeing 787 Dreamliner, numberOfCrew, 2 (flight deck)]
-
A.
crewCountApproximate
Indicates that the relationship specifies an estimated or approximate number of crew members associated with an entity.
-
B.
hasCrewCapacity
chosen
Indicates that an entity is capable of accommodating a specified number of crew members.
-
C.
crew
Indicates that one entity serves as the group of people who operate, staff, or work on another entity (such as a vehicle, vessel, or production).
-
D.
crewType
Indicates the specific role or category of crew associated with an entity, such as the type of personnel assigned to operate or support it.
-
E.
intendedCrewVehicle
Indicates that a particular vehicle is designated or planned to be used by a specific crew.
- 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_69a4940542308190ac2a0b1f730b7cfc |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4b99d1e8c81909cf1178d68d38885 |
completed | March 1, 2026, 10:11 p.m. |
| PD | Predicate disambiguation | batch_69a4b743175481908f3967e589717c55 |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:42 p.m.