Triple
T6055557
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SLAC Large Detector |
E134900
|
entity |
| Predicate | acceleratorTypeUsed |
P17456
|
FINISHED |
| Object | linear accelerator |
—
|
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: linear accelerator | Statement: [SLAC Large Detector, acceleratorTypeUsed, linear accelerator]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: acceleratorTypeUsed Context triple: [SLAC Large Detector, acceleratorTypeUsed, linear accelerator]
-
A.
acceleratorType
chosen
Indicates the kind or category of accelerator associated with or used by an entity.
-
B.
gpuType
Indicates the specific kind or model category of GPU associated with an entity.
-
C.
neuralEngineType
Indicates the specific kind or category of neural processing engine associated with or used by an entity.
-
D.
usedAccelerator
Indicates that an entity has applied or made use of an accelerator (such as a device, mechanism, or process) to increase speed, performance, or progress in relation to another entity or activity.
-
E.
computingModel
Indicates the computational framework, paradigm, or formal system used to perform or describe a computation.
- 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_69c00877b6d4819096b0e163728b73a3 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c0570a863c819090291775245708d6 |
completed | March 22, 2026, 8:54 p.m. |
| PD | Predicate disambiguation | batch_69c049edc6f0819092ca620d9073ad26 |
completed | March 22, 2026, 7:58 p.m. |
Created at: March 22, 2026, 4:09 p.m.