Triple
T19382178
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hazard Avoidance Cameras |
E484838
|
entity |
| Predicate | usesImagingType |
P52562
|
FINISHED |
| Object | stereo imaging |
—
|
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: stereo imaging | Statement: [Hazard Avoidance Cameras, usesImagingType, stereo imaging]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesImagingType Context triple: [Hazard Avoidance Cameras, usesImagingType, stereo imaging]
-
A.
hasImagingType
chosen
Indicates the specific imaging modality or technique associated with or used in a given imaging procedure or result.
-
B.
usesCameraType
Indicates that one entity employs or operates a specific type or category of camera.
-
C.
usesImagery
Indicates that one entity employs descriptive or figurative language to create sensory or vivid mental images in relation to another entity or concept.
-
D.
usesImageModel
Indicates that one entity employs or relies on an image-based model (such as a computer vision or image generation model) in relation to another entity or task.
-
E.
usesPhotographyFrom
Indicates that one entity employs or incorporates photographic material originating from another entity.
- 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_69d8e8d460d88190abf0591c5c9d2b0c |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e61a6075a88190aed7afa3b8fd3021 |
completed | April 20, 2026, 12:21 p.m. |
| PD | Predicate disambiguation | batch_69e4fd602f008190aa9bc76ae17e4ce1 |
completed | April 19, 2026, 4:05 p.m. |
Created at: April 10, 2026, 1:35 p.m.