Triple
T18724416
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GPT |
E457859
|
entity |
| Predicate | usesTrainingObjective |
P12747
|
FINISHED |
| Object | next token prediction |
—
|
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: next token prediction | Statement: [GPT, usesTrainingObjective, next token prediction]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesTrainingObjective Context triple: [GPT, usesTrainingObjective, next token prediction]
-
A.
trainingObjective
chosen
Indicates the goal or target outcome that a training process is designed to achieve.
-
B.
usesTrainingStrategy
Indicates that one entity applies or follows a particular training strategy in carrying out its learning or optimization process.
-
C.
hasTrainingFunction
Indicates that one entity serves as a training function or mechanism for another entity.
-
D.
usesObjective
Indicates that an agent employs or applies a particular object, tool, or resource to carry out an action or achieve a goal.
-
E.
trainingUse
Indicates that something is used for training purposes, such as preparing, educating, or improving the skills or performance of an entity.
- 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_69d8d393ba9c8190a8b03b04ddbb0a09 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e56d72d2c4819080b0d31860976b5e |
completed | April 20, 2026, 12:04 a.m. |
| PD | Predicate disambiguation | batch_69e48d03766c8190a43f7681842f4f8d |
completed | April 19, 2026, 8:06 a.m. |
Created at: April 10, 2026, 11:50 a.m.