Triple
T18205281
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | VisionEncoderDecoderModel |
E435885
|
entity |
| Predicate | usesAttentionMechanism |
P57896
|
FINISHED |
| Object | True |
—
|
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: True | Statement: [VisionEncoderDecoderModel, usesAttentionMechanism, True]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesAttentionMechanism Context triple: [VisionEncoderDecoderModel, usesAttentionMechanism, True]
-
A.
usesNeuralNetworks
Indicates that one entity employs neural network models or techniques as part of its functioning, processing, or decision-making.
-
B.
usesLearningMechanism
Indicates that one entity employs or applies a particular learning mechanism or method in its functioning or behavior.
-
C.
hasNeuralNetwork
Indicates that an entity possesses, incorporates, or is equipped with a neural network.
-
D.
focusMechanism
chosen
Indicates the method or process by which attention or emphasis is directed toward a particular entity or aspect within a context.
-
E.
activationMechanism
Indicates the process or method by which one entity initiates, triggers, or enables the activity or functioning of another 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_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.