Triple
T965388
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Whisper |
E20825
|
entity |
| Predicate | hasModelVariant |
P455
|
FINISHED |
| Object | tiny |
—
|
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: tiny | Statement: [Whisper, hasModelVariant, tiny]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasModelVariant Context triple: [Whisper, hasModelVariant, tiny]
-
A.
hasVariant
chosen
Indicates that one entity exists as an alternative form, version, or variation of another entity.
-
B.
hasVariantSeries
Indicates a relationship where one entity is a variant or alternative series derived from or associated with another series.
-
C.
hasModelType
Indicates that an entity is associated with or classified under a specific model type.
-
D.
hasReleaseModel
Indicates the type or strategy of release associated with an entity, such as how or under what model it is made available.
-
E.
hasFranchiseModel
Indicates that one entity operates under, offers, or is associated with a business franchise system or structure defined by 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_69a493b33d2c81909c52c369d3ca8436 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b431d61481908b53490e99670363 |
completed | March 1, 2026, 9:48 p.m. |
| PD | Predicate disambiguation | batch_69a4b2a42c1481908d940cbe0aefdd3b |
completed | March 1, 2026, 9:41 p.m. |
Created at: March 1, 2026, 7:40 p.m.