Triple
T22971237
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beagle |
E571190
|
entity |
| Predicate | subjectAircraftTypicalCrewPositions |
P21064
|
FINISHED |
| Object | pilot |
—
|
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: pilot | Statement: [Beagle, subjectAircraftTypicalCrewPositions, pilot]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: subjectAircraftTypicalCrewPositions Context triple: [Beagle, subjectAircraftTypicalCrewPositions, pilot]
-
A.
typicalPilotPosition
Indicates the usual or standard spatial position or placement where a pilot is located relative to the associated object or system.
-
B.
crewType
chosen
Indicates the specific role or category of crew associated with an entity, such as the type of personnel assigned to operate or support it.
-
C.
airWingPersonnel
Indicates that the subject is a member of, or assigned as personnel to, an air wing unit.
-
D.
crewComplementType
Indicates the classification or category of a crew complement associated with an entity (such as its role, composition, or staffing type).
-
E.
cockpitType
Indicates the specific configuration or style of cockpit associated with an entity (e.g., vehicle or aircraft).
- 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_69e245b2c6548190a0e4c7f2f7df2d48 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1823370fc819084a13d6e4eb6e44e |
completed | April 29, 2026, 3:59 a.m. |
| PD | Predicate disambiguation | batch_69ef3b9101f48190a06c69dff26c6441 |
completed | April 27, 2026, 10:33 a.m. |
Created at: April 17, 2026, 3:48 p.m.