Triple
T30382174
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Huawei P20 Pro |
E772860
|
entity |
| Predicate | lowLightPhotographyPerformance |
P169136
|
FINISHED |
| Object | strong |
—
|
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: strong | Statement: [Huawei P20 Pro, lowLightPhotographyPerformance, strong]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lowLightPhotographyPerformance Context triple: [Huawei P20 Pro, lowLightPhotographyPerformance, strong]
-
A.
sensitivityToLight
Indicates a relationship where an entity reacts adversely or more strongly than normal when exposed to light.
-
B.
visibleInLongExposureImages
Indicates that the subject can be detected or seen when images are captured using long exposure photography settings.
-
C.
illuminationCondition
Indicates the lighting or brightness conditions under which an event, observation, or interaction takes place.
-
D.
lightRange
Indicates the distance or area over which a light source effectively emits or illuminates.
-
E.
bestTimeForPhotography
Indicates the most suitable or optimal time period for taking photographs, typically based on lighting or environmental conditions.
- 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_69f2248e3444819081b05712dc6873de |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f6851aeb948190b674200cf6bf51f9 |
completed | May 2, 2026, 11:13 p.m. |
| PD | Predicate disambiguation | batch_69f678d019fc8190913662cd2f87b857 |
completed | May 2, 2026, 10:21 p.m. |
| PDg | Predicate description generation | batch_69f679496c188190ba585792f987a1f4 |
completed | May 2, 2026, 10:23 p.m. |
Created at: April 29, 2026, 8 p.m.