Triple
T4424854
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CLIP |
E95184
|
entity |
| Predicate | pretrainingDataType |
P21226
|
FINISHED |
| Object | image-text pairs |
—
|
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: image-text pairs | Statement: [CLIP, pretrainingDataType, image-text pairs]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: pretrainingDataType Context triple: [CLIP, pretrainingDataType, image-text pairs]
-
A.
trainingDataType
chosen
Indicates the type or category of data used for training a model, system, or process.
-
B.
pretrainingRole
Indicates the role or function an entity serves specifically during a pretraining phase or process.
-
C.
trainingDataSource
Indicates the origin or provider from which the training data for a model or system is obtained.
-
D.
trainingDataIncludes
Indicates that one entity’s training dataset contains or incorporates the other entity as part of its data.
-
E.
trainingModel
Indicates that an entity is engaged in the process of teaching, adjusting, or optimizing a model using data or experience.
- 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_69b3453c2a0c8190926b574c90766db9 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3554ca5208190ba2661616dcf071c |
completed | March 13, 2026, 12:07 a.m. |
| PD | Predicate disambiguation | batch_69b34f5eabe88190a12b244ea71e46d6 |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:30 p.m.