Triple
T816556
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TensorFlow |
E17662
|
entity |
| Predicate | supportsModelType |
P19966
|
FINISHED |
| Object | convolutional neural networks |
—
|
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 networks | Statement: [TensorFlow, supportsModelType, convolutional neural networks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: supportsModelType Context triple: [TensorFlow, supportsModelType, convolutional neural networks]
-
A.
supportsProjectType
Indicates that one entity is capable of handling, accommodating, or being compatible with a specified type of project.
-
B.
supportsMissionType
Indicates that one entity is capable of handling, enabling, or being compatible with a specified type of mission.
-
C.
supportsProduct
Indicates that one entity provides assistance, compatibility, or necessary resources for the operation, use, or maintenance of a specified product.
-
D.
hasModelType
Indicates that an entity is associated with or classified under a specific model type.
-
E.
isSupportedBy
Indicates that an entity is upheld, sustained, or enabled by another entity, which provides necessary assistance, resources, or justification.
- F. None of above. chosen
Provenance (4 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_69a4937bcaac8190a322524ac6f45a5a |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4ab621d2c819083f10bff4f66c482 |
completed | March 1, 2026, 9:10 p.m. |
| PD | Predicate disambiguation | batch_69a4aa756920819080ae82948974c876 |
completed | March 1, 2026, 9:07 p.m. |
| PDg | Predicate description generation | batch_69a4ab4781c88190ae36906251347cdc |
completed | March 1, 2026, 9:10 p.m. |
Created at: March 1, 2026, 7:38 p.m.