Triple
T31498139
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | S-Cinetone |
E803603
|
entity |
| Predicate | whiteBalanceConsideration |
P171708
|
FINISHED |
| Object | benefits from accurate white balance in-camera |
—
|
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: benefits from accurate white balance in-camera | Statement: [S-Cinetone, whiteBalanceConsideration, benefits from accurate white balance in-camera]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: whiteBalanceConsideration Context triple: [S-Cinetone, whiteBalanceConsideration, benefits from accurate white balance in-camera]
-
A.
whitePoint
Indicates the reference color point or standard white used as a basis for color measurements or calibration in a color space.
-
B.
exposureCompensation
Indicates an adjustment applied to increase or decrease the overall brightness of an exposure relative to the camera’s metered value.
-
C.
brightnessCorrelatesWith
Indicates that changes in the brightness of one entity are systematically associated with changes in the brightness of another entity.
-
D.
brightnessVariation
Indicates a change or fluctuation in the level of brightness of an entity over time or across conditions.
-
E.
sensitivityToLight
Indicates a relationship where an entity reacts adversely or more strongly than normal when exposed to light.
- F. None of above. chosen
Provenance (4 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_69f348cae52081909fa8e5f697523ae3 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6a1eac8688190afdf5732cedf086d |
completed | May 3, 2026, 1:16 a.m. |
| PD | Predicate disambiguation | batch_69f69fe82e5c81909da9db0a2f3bba6d |
completed | May 3, 2026, 1:07 a.m. |
| PDg | Predicate description generation | batch_69f6a0e920cc8190a943fdd0594906c5 |
completed | May 3, 2026, 1:12 a.m. |
Created at: April 30, 2026, 9:42 p.m.