Triple
T18205261
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | VisionEncoderDecoderModel |
E435885
|
entity |
| Predicate | encoderType |
P55910
|
FINISHED |
| Object | vision model |
—
|
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: vision model | Statement: [VisionEncoderDecoderModel, encoderType, vision model]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: encoderType Context triple: [VisionEncoderDecoderModel, encoderType, vision model]
-
A.
textEncoderType
Indicates the specific kind or configuration of encoder used to process or represent text in a system or model.
-
B.
imageEncoderType
chosen
Indicates the specific kind or configuration of encoder used to process and represent image data.
-
C.
codingSystemType
Indicates the classification or category of coding system used to encode or represent information in a given context.
-
D.
binaryType
Indicates that something is classified as a binary type, typically distinguishing between two mutually exclusive categories or values.
-
E.
embeddingType
Indicates the specific kind or category of embedding representation used to encode an entity or data.
- 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_69d8b90dba6481908e119eb9aa4ca0cb |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4e222831081908f7d5500424e3acb |
completed | April 19, 2026, 2:09 p.m. |
| PD | Predicate disambiguation | batch_69e4332155d88190b106d0dceb4554af |
completed | April 19, 2026, 1:42 a.m. |
Created at: April 10, 2026, 10:32 a.m.