Triple
T31498138
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | S-Cinetone |
E803603
|
entity |
| Predicate | exposureRecommendation |
P171707
|
FINISHED |
| Object | expose normally using camera metering |
—
|
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: expose normally using camera metering | Statement: [S-Cinetone, exposureRecommendation, expose normally using camera metering]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: exposureRecommendation Context triple: [S-Cinetone, exposureRecommendation, expose normally using camera metering]
-
A.
exposureLevel
Indicates the degree or intensity to which an entity is subjected or exposed to a particular factor, condition, or influence.
-
B.
exposureType
Indicates the specific manner or context in which one entity is exposed to another entity, condition, or influence.
-
C.
resultOfExposure
Indicates that something occurs or exists as a consequence of being exposed to a particular agent, condition, or environment.
-
D.
providesExposureTo
Indicates that one entity gives another entity the opportunity to be seen, noticed, or become known by a particular audience, environment, or set of influences.
-
E.
safetyAdvice
Indicates that one entity provides guidance or recommendations to another entity about how to avoid danger or reduce risk in a particular context.
- 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.