Triple
T29636249
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Contrastive Predictive Coding |
E755722
|
entity |
| Predicate | trainingSignalSource |
P21073
|
FINISHED |
| Object | data itself |
—
|
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: data itself | Statement: [Contrastive Predictive Coding, trainingSignalSource, data itself]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainingSignalSource Context triple: [Contrastive Predictive Coding, trainingSignalSource, data itself]
-
A.
trainingUse
Indicates that something is used for training purposes, such as preparing, educating, or improving the skills or performance of an entity.
-
B.
trainingDataSource
chosen
Indicates the origin or provider from which the training data for a model or system is obtained.
-
C.
trainingMethod
Indicates the specific approach, technique, or procedure used to train an entity (such as a person, model, or system).
-
D.
trainingModality
Indicates the method or format through which training or instruction is delivered or conducted.
-
E.
trainingLeadsTo
Indicates that a process of training results in or brings about a particular outcome, state, or effect.
- 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_69f0ef88fbe081908f0ad90c1c413f1c |
completed | April 28, 2026, 5:34 p.m. |
| NER | Named-entity recognition | batch_69f69dfdda708190be290c7bec205445 |
completed | May 3, 2026, 12:59 a.m. |
| PD | Predicate disambiguation | batch_69f69d1a37e081908d1d86b90ff502bd |
completed | May 3, 2026, 12:55 a.m. |
Created at: April 28, 2026, 6:44 p.m.