Triple
T35850665
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apple Night mode |
E1036343
|
entity |
| Predicate | exposureComputation |
P184208
|
FINISHED |
| Object | automatically selects base exposure time |
—
|
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: automatically selects base exposure time | Statement: [Apple Night mode, exposureComputation, automatically selects base exposure time]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: exposureComputation Context triple: [Apple Night mode, exposureComputation, automatically selects base exposure time]
-
A.
visibilityComputation
Indicates the process or relationship by which the visibility of one entity from another is determined or computed.
-
B.
exposureType
Indicates the specific manner or context in which one entity is exposed to another entity, condition, or influence.
-
C.
exposureLevel
Indicates the degree or intensity to which an entity is subjected or exposed to a particular factor, condition, or influence.
-
D.
exposureTime
Indicates the duration for which a subject or object is exposed to a particular condition, influence, or medium.
-
E.
exposureCompensation
Indicates an adjustment applied to increase or decrease the overall brightness of an exposure relative to the camera’s metered value.
- 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_69f76e1b4aa481909630373171eb5ec6 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7acaec1508190a38f2ac9cc5383e7 |
completed | May 3, 2026, 8:14 p.m. |
| PD | Predicate disambiguation | batch_69f7ab734d848190a84f9b8c3a952b75 |
completed | May 3, 2026, 8:09 p.m. |
| PDg | Predicate description generation | batch_69f7ac2210e481909279dade5328825c |
completed | May 3, 2026, 8:12 p.m. |
Created at: May 3, 2026, 4:06 p.m.