Triple
T35850664
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apple Night mode |
E1036343
|
entity |
| Predicate | sceneAnalysis |
P183900
|
FINISHED |
| Object | automatically detects low-light scenes |
—
|
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 detects low-light scenes | Statement: [Apple Night mode, sceneAnalysis, automatically detects low-light scenes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sceneAnalysis Context triple: [Apple Night mode, sceneAnalysis, automatically detects low-light scenes]
-
A.
sceneFeature
Indicates a characteristic, element, or attribute that is present within or helps define a particular scene.
-
B.
sceneLabel
Indicates the categorical label or type assigned to an entire scene based on its overall content or context.
-
C.
visualFeature
Indicates a relationship where one entity possesses or exhibits a particular visual characteristic or attribute of another entity.
-
D.
sceneDepicts
Indicates that a scene visually represents or portrays a particular entity, event, or situation.
-
E.
sceneStatus
Indicates the current state or condition of a scene within a given context or process.
- 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_69f7aaabb58c8190bf81673608ecfb6e |
completed | May 3, 2026, 8:06 p.m. |
| PD | Predicate disambiguation | batch_69f7a8d435288190b30b1991fb003121 |
completed | May 3, 2026, 7:58 p.m. |
| PDg | Predicate description generation | batch_69f7aa33e0488190a135166bb67e1118 |
completed | May 3, 2026, 8:04 p.m. |
Created at: May 3, 2026, 4:06 p.m.