Triple
T12048454
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apple A17 Pro |
E286847
|
entity |
| Predicate | transistorCountApproximate |
P86699
|
FINISHED |
| Object | 19 billion |
—
|
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: 19 billion | Statement: [Apple A17 Pro, transistorCountApproximate, 19 billion]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: transistorCountApproximate Context triple: [Apple A17 Pro, transistorCountApproximate, 19 billion]
-
A.
transistorCountApprox
chosen
Indicates an approximate relationship specifying the estimated number of transistors associated with an entity.
-
B.
transistorCount
Indicates the number of transistors contained within an electronic component or device.
-
C.
approximateProductionNumber
Indicates that one entity specifies an estimated or non-exact quantity associated with the production output of another entity.
-
D.
dataPinCount
Indicates the number of data pins associated with or used by an entity in a given context.
-
E.
pinCount
Indicates the number of pins associated with or assigned to a given entity.
- 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_69d6ab4780948190bdb9f7620c2ac27e |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9100b4ca8819084845ca4c13e34ce |
completed | April 10, 2026, 2:58 p.m. |
| PD | Predicate disambiguation | batch_69d902bac9e08190aa1a99c835f29542 |
completed | April 10, 2026, 2:01 p.m. |
Created at: April 8, 2026, 9:47 p.m.