Triple
T1049901
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Three stripes |
E22670
|
entity |
| Predicate | appliesToProductCategory |
P15481
|
FINISHED |
| Object | sportswear |
—
|
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: sportswear | Statement: [Three stripes, appliesToProductCategory, sportswear]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: appliesToProductCategory Context triple: [Three stripes, appliesToProductCategory, sportswear]
-
A.
appliesTo
Indicates that something is relevant, valid, or has effect in relation to a particular entity, case, or context.
-
B.
hasCategoryOn
chosen
Indicates that something is assigned to or associated with a specific category within a given context or scope.
-
C.
appliesToFeature
Indicates that something (such as a rule, constraint, or configuration) is relevant to, or governs, a specific feature.
-
D.
hasRetailCategory
Indicates that an entity is associated with a specific retail category or type of retail business.
-
E.
hasSeparateSupportingCategory
Indicates that an entity is associated with an additional, distinct category used specifically for supporting or auxiliary classification purposes separate from its primary category.
- 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_69a493da02e081908c13ff5e02a0fe7a |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b8b2c6208190b6fdf3e93b1b1d04 |
completed | March 1, 2026, 10:07 p.m. |
| PD | Predicate disambiguation | batch_69a4b7309cc481908ed839b0b8d75dbf |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:42 p.m.