Triple
T8577262
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Parallel WaveNet |
E203077
|
entity |
| Predicate | teacherModel |
P58554
|
FINISHED |
| Object | autoregressive WaveNet |
—
|
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: autoregressive WaveNet | Statement: [Parallel WaveNet, teacherModel, autoregressive WaveNet]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: teacherModel Context triple: [Parallel WaveNet, teacherModel, autoregressive WaveNet]
-
A.
trainerModel
chosen
Indicates that one entity serves as the trainer or training source for a model entity.
-
B.
teacherOrInfluence
Indicates that one entity serves as a teacher to, or has a significant influence on the development, behavior, or thinking of, another entity.
-
C.
hasTeacher
Indicates that one entity serves as an instructor or educator for another entity.
-
D.
taughtAs
Indicates that one entity served as a teacher or instructor for another entity in an educational or training context.
-
E.
trainer
Indicates a relationship where one entity teaches, coaches, or prepares another entity to develop skills, knowledge, or performance in a particular domain.
- 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_69ca8328ebe481909a8c038fa79959b4 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbea97787481909ebbaa45f59cbdaa |
completed | March 31, 2026, 3:39 p.m. |
| PD | Predicate disambiguation | batch_69cbd11b13108190b07f8f161425a585 |
completed | March 31, 2026, 1:50 p.m. |
Created at: March 30, 2026, 6:22 p.m.