Triple
T30358199
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nikon FTZ mount adapter |
E772202
|
entity |
| Predicate | supportsVibrationReductionWith |
P207239
|
FINISHED |
| Object | Nikon VR lenses |
E772203
|
NE 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: Nikon VR lenses | Statement: [Nikon FTZ mount adapter, supportsVibrationReductionWith, Nikon VR lenses]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: supportsVibrationReductionWith Context triple: [Nikon FTZ mount adapter, supportsVibrationReductionWith, Nikon VR lenses]
-
A.
supportsVibration
Indicates that one entity provides the capability or functionality to handle, generate, or respond to vibration for another entity.
-
B.
hasHDVibration
Indicates that an entity provides or supports high-definition (precise, nuanced) vibration feedback in its interactions or operations.
-
C.
hasOpticalImageStabilization
Indicates that a device or component includes a feature that reduces image blur caused by camera movement during capture.
-
D.
noiseReductionFeature
Indicates that an entity includes or supports a capability to reduce or minimize unwanted noise.
-
E.
supportsFallDetection
Indicates that an entity provides functionality to detect when a fall has occurred.
- F. None of above. chosen
Provenance (5 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_69f2248c6f5c8190a6177842bf791a3c |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_6a037c9141dc819098d7fcc36e69882c |
completed | May 12, 2026, 7:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a277c30dc8081909f1ae12e7af8c443 |
completed | June 9, 2026, 2:36 a.m. |
| PD | Predicate disambiguation | batch_6a0379e2fac0819089b522db3260028c |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c7ee0388190a29faeb5cdb0950a |
completed | May 12, 2026, 7:16 p.m. |
Created at: April 29, 2026, 7:57 p.m.