Triple
T36491811
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | miniImageNet |
E899067
|
entity |
| Predicate | hasInputShape |
P55165
|
FINISHED |
| Object | 3x84x84 (channels-first) in many implementations |
—
|
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: 3x84x84 (channels-first) in many implementations | Statement: [miniImageNet, hasInputShape, 3x84x84 (channels-first) in many implementations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasInputShape Context triple: [miniImageNet, hasInputShape, 3x84x84 (channels-first) in many implementations]
-
A.
inputShape
chosen
Indicates the geometric form or configuration that is provided as input to a process, function, or system.
-
B.
hasInputSize
Indicates that an entity is associated with, or requires, a specific size or dimensionality for its input.
-
C.
hasInput
Indicates that an entity receives or takes another entity as an input to its process, function, or operation.
-
D.
hasShapeModel
Indicates that an entity is associated with a specific geometric or structural shape model that represents its form.
-
E.
hasComponentShape
Indicates that an entity includes, contains, or is characterized by a specific geometric or structural shape as one of its components.
- 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_69f76e5ad4588190bdbce60c52fbb785 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a00b3d2e0c88190a8bf49576e654f64 |
completed | May 10, 2026, 4:35 p.m. |
| PD | Predicate disambiguation | batch_6a00b376f4e48190a6676779fe02ea9f |
completed | May 10, 2026, 4:33 p.m. |
Created at: May 3, 2026, 4:10 p.m.