Triple
T12144359
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Givry |
E289276
|
entity |
| Predicate | typicalStyleWhite |
P90210
|
FINISHED |
| Object | fresh |
—
|
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: fresh | Statement: [Givry, typicalStyleWhite, fresh]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalStyleWhite Context triple: [Givry, typicalStyleWhite, fresh]
-
A.
typicalWhiteStyleDescriptor
Indicates that something is described as having characteristics commonly associated with a stereotypical white cultural style or aesthetic.
-
B.
styleWhite
chosen
Indicates that one entity has a white style, appearance, or coloration in relation to another or within a given context.
-
C.
typicalVisualStyle
Indicates the characteristic or commonly observed visual appearance or aesthetic style associated with an entity.
-
D.
uniformStyle
Indicates that the related entities share the same or a consistent style, pattern, or formatting.
-
E.
primaryWhiteVariety
Indicates that one entity is the primary white (light-skinned or white-colored) variety or form of 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_69d6ab4c6710819097a9d228382dde43 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d91841615c819097f20a7447a1b8f4 |
completed | April 10, 2026, 3:33 p.m. |
| PD | Predicate disambiguation | batch_69d91508f8008190b3a90ec0bf0953ca |
completed | April 10, 2026, 3:19 p.m. |
Created at: April 8, 2026, 9:49 p.m.