Triple
T1690297
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Surface Duo |
E36535
|
entity |
| Predicate | cameraType |
P29799
|
FINISHED |
| Object | single front-facing camera used for rear and front shots |
—
|
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: single front-facing camera used for rear and front shots | Statement: [Surface Duo, cameraType, single front-facing camera used for rear and front shots]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cameraType Context triple: [Surface Duo, cameraType, single front-facing camera used for rear and front shots]
-
A.
cameraStyle
Indicates the characteristic visual approach or technique used by a camera in capturing or presenting imagery.
-
B.
captureType
Indicates the manner or method by which something is captured, recorded, or acquired in the context of the relationship.
-
C.
hasCamera
Indicates that an entity is equipped with or possesses a camera.
-
D.
laterLensType
Indicates that one lens type occurs or is used at a later time than another lens type in a temporal sequence.
-
E.
rearCameraFeature
chosen
Indicates that an entity has a specific characteristic, capability, or attribute related to its rear-facing camera.
- F. None of above.
Provenance (3 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_69a886151508819084fa7f1ce6e05577 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aaf3359ce48190803b322db8ad6027 |
completed | March 6, 2026, 3:31 p.m. |
| PD | Predicate disambiguation | batch_69aa61b71cec8190b273588051058ebd |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:29 p.m.