Triple
T19921574
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DJI Avata |
E478807
|
entity |
| Predicate | cameraResolutionVideo |
P15682
|
FINISHED |
| Object | 4K |
—
|
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: 4K | Statement: [DJI Avata, cameraResolutionVideo, 4K]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cameraResolutionVideo Context triple: [DJI Avata, cameraResolutionVideo, 4K]
-
A.
hasCameraResolution
Indicates that an entity is associated with a specific camera resolution value or specification.
-
B.
rearCameraMainResolution
Indicates the primary resolution (in megapixels or similar units) of a device’s main rear-facing camera.
-
C.
sensorResolution
Indicates the level of detail or precision with which a sensor can measure or distinguish changes in the observed quantity or environment.
-
D.
videoQuality
chosen
Indicates the level or standard of clarity, resolution, and overall visual fidelity associated with a given video.
-
E.
viewfinderResolution
Indicates the resolution or level of detail provided by a device’s viewfinder display.
- 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_69d8e521855c8190b41871700afc8d6a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e659c6919c8190a96106532580b6b6 |
completed | April 20, 2026, 4:52 p.m. |
| PD | Predicate disambiguation | batch_69e537f070b481908958e0e5911dcdc1 |
completed | April 19, 2026, 8:15 p.m. |
Created at: April 10, 2026, 1:53 p.m.