Triple
T32104805
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Borshchiv |
E819952
|
entity |
| Predicate | hasTraditionalGarment |
P66644
|
FINISHED |
| Object | embroidered shirts |
—
|
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: embroidered shirts | Statement: [Borshchiv, hasTraditionalGarment, embroidered shirts]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTraditionalGarment Context triple: [Borshchiv, hasTraditionalGarment, embroidered shirts]
-
A.
hasTraditionalAttire
chosen
Indicates that an entity possesses or is associated with clothing that is customary or traditional within a particular culture or community.
-
B.
traditionalDressVariant
Indicates that one traditional dress is a variant or localized form of another traditional dress within the same broader cultural or stylistic tradition.
-
C.
traditionalClothingType
Indicates the type or category of traditional clothing associated with an entity.
-
D.
hasGarment
Indicates that one entity possesses, wears, or is associated with a particular garment.
-
E.
traditionalDressSimilarTo
Indicates that one traditional dress resembles or shares notable stylistic or cultural features with another traditional dress.
- 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_69f34901106881908ea893ad504a08be |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69feecf1bb248190ba30f0bb1d22ee08 |
completed | May 9, 2026, 8:14 a.m. |
| PD | Predicate disambiguation | batch_69feea5f27748190b223ee4e3ba5a678 |
completed | May 9, 2026, 8:03 a.m. |
Created at: May 1, 2026, 12:26 a.m.