Triple
T36489821
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LRCN |
E899022
|
entity |
| Predicate | canUsePretrained |
P118074
|
FINISHED |
| Object | CNN backbone |
—
|
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: CNN backbone | Statement: [LRCN, canUsePretrained, CNN backbone]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: canUsePretrained Context triple: [LRCN, canUsePretrained, CNN backbone]
-
A.
supportsPretrainedModels
chosen
Indicates that an entity provides compatibility with or functionality for using pretrained models.
-
B.
pretrained
Indicates that a model or system has been previously trained on data before being used for its current task or context.
-
C.
pretrainedOn
Indicates that a model has been trained in advance using a specified dataset or data source before being applied to downstream tasks.
-
D.
canBeFineTuned
Indicates that one entity (typically a model or system) is capable of being further trained or adjusted using additional data or tasks to improve or specialize its behavior.
-
E.
pretrainingType
Indicates the specific kind or category of pretraining process that has been applied to an entity (such as a model or system).
- 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.