Triple
T2665053
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apple A17 Pro |
E55614
|
entity |
| Predicate | supportsMeshShading |
P41281
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Apple A17 Pro, supportsMeshShading, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: supportsMeshShading Context triple: [Apple A17 Pro, supportsMeshShading, true]
-
A.
usesLightingFor
Indicates that one entity employs or relies on a particular lighting setup, technology, or condition to achieve a purpose or perform an action.
-
B.
hasLighting
Indicates that one entity is equipped with, contains, or is characterized by a particular type or configuration of lighting.
-
C.
hardwareAcceleration
Indicates that an operation or process is executed using specialized hardware resources (such as GPU or dedicated accelerators) rather than relying solely on general-purpose CPU computation.
-
D.
supportsColorSampling
Indicates that one entity can perform or accommodate color sampling operations on another entity or its data.
-
E.
appliedToMaterial
Indicates that an action, process, or treatment is performed on or directed toward a specific material.
- 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_69ab49e54de48190be708cd1cf8be073 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd96ed2748190a4feae98199b459d |
completed | March 7, 2026, 7:53 a.m. |
| PD | Predicate disambiguation | batch_69abd81768748190bd965f367cf6ef37 |
completed | March 7, 2026, 7:47 a.m. |
| PDg | Predicate description generation | batch_69abd8dd05c48190b4f90031642c4091 |
completed | March 7, 2026, 7:50 a.m. |
Created at: March 6, 2026, 9:54 p.m.