Triple
T20270448
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Operational Conversion Unit for F-35B Lightning II |
E499077
|
entity |
| Predicate | usesAircraftFeature |
P139485
|
FINISHED |
| Object | sensor fusion |
—
|
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: sensor fusion | Statement: [Operational Conversion Unit for F-35B Lightning II, usesAircraftFeature, sensor fusion]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesAircraftFeature Context triple: [Operational Conversion Unit for F-35B Lightning II, usesAircraftFeature, sensor fusion]
-
A.
usesCarrierAircraft
Indicates that one entity employs or operates aircraft that are designed to be launched from and recovered by an aircraft carrier.
-
B.
usesAircraftVariant
Indicates that one entity operates or employs a specific variant or version of an aircraft.
-
C.
supportsAircraft
Indicates that one entity is capable of accommodating, carrying, or enabling the operation of an aircraft.
-
D.
usedOnAircraftName
Indicates that something is employed or applied on an aircraft identified by a specific name.
-
E.
usedByAircraftType
Indicates that something (such as equipment, infrastructure, or a procedure) is employed or operated by a specific type or category of aircraft.
- 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_69da6275fa6c8190952924930adee150 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e675dd7c58819095bbf4baeda04d6a |
completed | April 20, 2026, 6:52 p.m. |
| PD | Predicate disambiguation | batch_69e55b1e5e1c8190ba8a5544b1db9e1d |
completed | April 19, 2026, 10:45 p.m. |
| PDg | Predicate description generation | batch_69e56702ad04819099c1c08f28d16809 |
completed | April 19, 2026, 11:36 p.m. |
Created at: April 11, 2026, 11:42 p.m.