Triple
T19190103
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GPT-1 |
E469810
|
entity |
| Predicate | pretrainingTask |
P134800
|
FINISHED |
| Object | language modeling |
—
|
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: language modeling | Statement: [GPT-1, pretrainingTask, language modeling]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: pretrainingTask Context triple: [GPT-1, pretrainingTask, language modeling]
-
A.
pretrainingType
Indicates the specific kind or category of pretraining process that has been applied to an entity (such as a model or system).
-
B.
pretrainingStage
Indicates that an entity is in, or associated with, an early training phase prior to its main or final training stage.
-
C.
pretrained
Indicates that a model or system has been previously trained on data before being used for its current task or context.
-
D.
pretrainingRole
Indicates the role or function an entity serves specifically during a pretraining phase or process.
-
E.
pretrainedOn
Indicates that a model has been trained in advance using a specified dataset or data source before being applied to downstream tasks.
- 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_69d8dd0ad9088190a173b32657ae2e7a |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5f8a16e20819080baa5112f000b41 |
completed | April 20, 2026, 9:57 a.m. |
| PD | Predicate disambiguation | batch_69e4b9bb158481909478ca2e06f3ba39 |
completed | April 19, 2026, 11:17 a.m. |
| PDg | Predicate description generation | batch_69e4bfe9ef7081908a74a57d1fc731ea |
completed | April 19, 2026, 11:43 a.m. |
Created at: April 10, 2026, 12:07 p.m.