Triple
T36523094
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Duff's device |
E900227
|
entity |
| Predicate | typicalUnrollFactor |
P204914
|
FINISHED |
| Object | 8 |
—
|
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: 8 | Statement: [Duff's device, typicalUnrollFactor, 8]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalUnrollFactor Context triple: [Duff's device, typicalUnrollFactor, 8]
-
A.
reductionFactor
Indicates the proportional amount or degree by which something is decreased relative to its original value or state.
-
B.
subdivisionFactor
Indicates how many smaller parts or segments a whole entity is divided into within a given context.
-
C.
rollOffFactor
Indicates how quickly the influence or intensity of something decreases as distance or another parameter increases.
-
D.
typicalNumberOfCycles
Indicates the usual or characteristic count of cycles associated with an entity, process, or event.
-
E.
typicalUseRatio
Indicates the proportion or share in which something is commonly or normally used relative to other possible uses or components.
- 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_69f76e5eedb88190a393b8c623f71dd7 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037c92f03c8190ae2751270b195423 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0bf4b88190bdcfae9a14b51f0a |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c82f8c88190bd77a086023ac0e1 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:11 p.m.