Triple
T1870173
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apple M1 Pro |
E39014
|
entity |
| Predicate | integratedNeuralEngine |
P11233
|
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: [Apple M1 Pro, integratedNeuralEngine, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: integratedNeuralEngine Context triple: [Apple M1 Pro, integratedNeuralEngine, yes]
-
A.
integratesNeuralEngine
chosen
Indicates that one entity incorporates or embeds a neural processing engine within its overall system or architecture.
-
B.
neuralEngineType
Indicates the specific kind or category of neural processing engine associated with or used by an entity.
-
C.
neuralEngineCores
Indicates the number or configuration of neural engine processing cores associated with a given hardware or system.
-
D.
neuralEnginePerformance
Indicates the level or efficiency of processing capability provided by a neural engine in performing AI or machine-learning tasks.
-
E.
integratesGPU
Indicates that one entity incorporates or embeds a GPU as a functional component within its design or system.
- 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_69a8862f7074819096afe7fe65e179e9 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb0f79fbc819085c54f3189a552d9 |
completed | March 7, 2026, 5 a.m. |
| PD | Predicate disambiguation | batch_69abafe2b56c81909e13d543982e6e13 |
completed | March 7, 2026, 4:56 a.m. |
Created at: March 4, 2026, 7:34 p.m.