Triple
T30481852
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Canon EOS R5 |
E775608
|
entity |
| Predicate | inBodyImageStabilizationStops |
P88978
|
FINISHED |
| Object | up to 8 stops |
—
|
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: up to 8 stops | Statement: [Canon EOS R5, inBodyImageStabilizationStops, up to 8 stops]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: inBodyImageStabilizationStops Context triple: [Canon EOS R5, inBodyImageStabilizationStops, up to 8 stops]
-
A.
hasOpticalImageStabilization
Indicates that a device or component includes a feature that reduces image blur caused by camera movement during capture.
-
B.
stabilizationCompensation
chosen
Indicates a compensatory action or mechanism that counteracts changes or disturbances in order to maintain or restore stability in a system or process.
-
C.
usesStills
Indicates that one entity employs or incorporates still images or photographs as part of its content, process, or representation.
-
D.
stoppedBy
Indicates that one entity causes another entity to cease moving, operating, or continuing an action.
-
E.
gimbalCapability
Indicates the ability of a system or device to support and control a gimbal’s movement or stabilization functions.
- 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_69f22497341481909c21ba329fadaa6b |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f68742cc6481908be525603fb6ba97 |
completed | May 2, 2026, 11:22 p.m. |
| PD | Predicate disambiguation | batch_69f678d2196c8190b9d0d2fcd47cc539 |
completed | May 2, 2026, 10:21 p.m. |
Created at: April 29, 2026, 8:12 p.m.