Triple
T7228788
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arafura-class offshore patrol vessel |
E154849
|
entity |
| Predicate | sensorFit |
P48715
|
FINISHED |
| Object | navigation radar |
—
|
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: navigation radar | Statement: [Arafura-class offshore patrol vessel, sensorFit, navigation radar]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sensorFit Context triple: [Arafura-class offshore patrol vessel, sensorFit, navigation radar]
-
A.
supportsHardwareCalibration
Indicates that one entity provides the capability or functionality to perform calibration operations on another entity’s hardware.
-
B.
sensorProvider
chosen
Indicates that one entity serves as the source or supplier of sensor data or sensing capabilities for another entity.
-
C.
wearableBy
Indicates that one entity is designed or suitable to be worn on the body by another entity.
-
D.
designedToMeasure
Indicates that one entity was intentionally created or configured for the purpose of quantifying, assessing, or evaluating another entity or its properties.
-
E.
noseGearFeature
Indicates that there is a specific characteristic, component, or design attribute associated with the nose landing gear of an 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_69c68811dd1c8190ac460bb39e64e1f0 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6e9e0ba248190a57a3b4fa8b858c7 |
completed | March 27, 2026, 8:34 p.m. |
| PD | Predicate disambiguation | batch_69c6e761b7fc8190857794d78af1b468 |
completed | March 27, 2026, 8:24 p.m. |
Created at: March 27, 2026, 2:54 p.m.