Triple
T8650075
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MacBook Air (M2, 2022) |
E205076
|
entity |
| Predicate | maxGpuCores |
P11227
|
FINISHED |
| Object | 10-core GPU |
—
|
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: 10-core GPU | Statement: [MacBook Air (M2, 2022), maxGpuCores, 10-core GPU]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maxGpuCores Context triple: [MacBook Air (M2, 2022), maxGpuCores, 10-core GPU]
-
A.
gpuCoreCount
chosen
Indicates the number of processing cores present in a GPU.
-
B.
maxCoresPerCluster
Indicates the maximum number of processing cores that are allowed or allocated within a single cluster.
-
C.
efficiencyCores
Indicates that the related cores are optimized for energy-efficient, low-power processing rather than maximum performance.
-
D.
neuralEngineCores
Indicates the number or configuration of neural engine processing cores associated with a given hardware or system.
-
E.
gpuComputePerformance
Indicates the level of processing capability a GPU can deliver for computational tasks, typically measured in operations per unit time.
- 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_69ca834e56848190abb0eeaec9dedd32 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc4813d0548190b203e594acc38c8f |
completed | March 31, 2026, 10:17 p.m. |
| PD | Predicate disambiguation | batch_69cc45619460819091e83ffdec99c865 |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:29 p.m.