Triple
T36491687
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Relation Networks for few-shot learning |
E899065
|
entity |
| Predicate | embeddingModuleType |
P79317
|
FINISHED |
| Object | convolutional neural network |
—
|
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: convolutional neural network | Statement: [Relation Networks for few-shot learning, embeddingModuleType, convolutional neural network]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: embeddingModuleType Context triple: [Relation Networks for few-shot learning, embeddingModuleType, convolutional neural network]
-
A.
embeddingType
Indicates the specific kind or category of embedding representation used to encode an entity or data.
-
B.
embeddedWith
Indicates that one entity is contained or integrated within another as a built-in or internal component.
-
C.
moduleType
chosen
Indicates the classification or category of a module in terms of its functional or structural type.
-
D.
extensionType
Indicates the specific kind or category of extension that characterizes how something is extended or augmented beyond its base form.
-
E.
embodiedBy
Indicates that an abstract concept, role, or function is physically or concretely realized in a specific 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_69f76e5ad4588190bdbce60c52fbb785 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7c371931c8190afb1d4dd5157f92c |
completed | May 3, 2026, 9:51 p.m. |
| PD | Predicate disambiguation | batch_69f7c1b91fd88190ab85afd626603769 |
completed | May 3, 2026, 9:44 p.m. |
Created at: May 3, 2026, 4:10 p.m.