Triple
T19957509
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | JPS Norton |
E479722
|
entity |
| Predicate | machineType |
P138024
|
FINISHED |
| Object | Norton rotary engine motorcycle |
—
|
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: Norton rotary engine motorcycle | Statement: [JPS Norton, machineType, Norton rotary engine motorcycle]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: machineType Context triple: [JPS Norton, machineType, Norton rotary engine motorcycle]
-
A.
acceleratorType
Indicates the kind or category of accelerator associated with or used by an entity.
-
B.
computingModel
Indicates the computational framework, paradigm, or formal system used to perform or describe a computation.
-
C.
computingResourceType
Indicates the specific kind or category of computing resource involved in the relationship or action.
-
D.
equipmentTypeTrainedOn
Indicates the type of equipment on which an entity has received training or is qualified to operate.
-
E.
gpuType
Indicates the specific kind or model category of GPU associated with an entity.
- F. None of above. chosen
Provenance (4 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_69d8e523c19881909f9197037200dde6 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65af1b32c81908ebe0e2570ec06a9 |
completed | April 20, 2026, 4:57 p.m. |
| PD | Predicate disambiguation | batch_69e537f7e4848190b431a69ec3f1b609 |
completed | April 19, 2026, 8:15 p.m. |
| PDg | Predicate description generation | batch_69e543c42c688190a22f4d31ec692377 |
completed | April 19, 2026, 9:06 p.m. |
Created at: April 10, 2026, 1:54 p.m.