Triple
T2646874
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | M2 Pro Mac mini |
E53804
|
entity |
| Predicate | hasNeuralEngine |
P31544
|
FINISHED |
| Object | 16‑core Neural Engine |
—
|
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: 16‑core Neural Engine | Statement: [M2 Pro Mac mini, hasNeuralEngine, 16‑core Neural Engine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNeuralEngine Context triple: [M2 Pro Mac mini, hasNeuralEngine, 16‑core Neural Engine]
-
A.
neuralEngineType
Indicates the specific kind or category of neural processing engine associated with or used by an entity.
-
B.
integratesNeuralEngine
Indicates that one entity incorporates or embeds a neural processing engine within its overall system or architecture.
-
C.
neuralEnginePerformance
Indicates the level or efficiency of processing capability provided by a neural engine in performing AI or machine-learning tasks.
-
D.
neuralEngineCores
chosen
Indicates the number or configuration of neural engine processing cores associated with a given hardware or system.
-
E.
NeuralEngineUseCases
Indicates the various tasks, scenarios, or applications in which a neural engine is employed or leveraged.
- 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_69ab495e192081909c77b622e8e7e15a |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd919bf2c81908feb768f3391e985 |
completed | March 7, 2026, 7:51 a.m. |
| PD | Predicate disambiguation | batch_69abd814298c8190952f05aed43f6bb8 |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:53 p.m.