Triple
T30315707
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | iMac Pro (2017) |
E771048
|
entity |
| Predicate | hasFaceTimeCamera |
P48612
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [iMac Pro (2017), hasFaceTimeCamera, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFaceTimeCamera Context triple: [iMac Pro (2017), hasFaceTimeCamera, yes]
-
A.
hasOuterSelfieCamera
Indicates that an entity (typically a device) is equipped with a front-facing camera intended for taking selfies.
-
B.
hasFaceUnlock
Indicates that an entity supports or is equipped with a facial recognition–based unlocking feature.
-
C.
has3DTouch
Indicates that one entity supports or is equipped with 3D Touch (pressure-sensitive touch input) functionality in relation to another entity or context.
-
D.
hasUltraWideCamera
Indicates that an entity is equipped with or includes an ultra-wide camera as one of its features.
-
E.
hasInnerSelfieCamera
chosen
Indicates that an object (typically a device) is equipped with a front-facing or inward-facing selfie 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_69f22488f224819081b0f3ec41ab975c |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f7c777e924819081a6634f549fe552 |
completed | May 3, 2026, 10:08 p.m. |
| PD | Predicate disambiguation | batch_69f7c475c58c8190a883554231e88c88 |
completed | May 3, 2026, 9:56 p.m. |
Created at: April 29, 2026, 7:51 p.m.