Triple
T13460871
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | shalwar kameez |
E311358
|
entity |
| Predicate | genderExpression |
P2577
|
FINISHED |
| Object | unisex garment |
—
|
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: unisex garment | Statement: [shalwar kameez, genderExpression, unisex garment]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genderExpression Context triple: [shalwar kameez, genderExpression, unisex garment]
-
A.
genderVariant
Indicates that an entity’s gender identity or expression differs from traditional or expected norms associated with their assigned sex or gender.
-
B.
genderConfiguration
Indicates how the genders of the involved entities are arranged or combined within a particular relationship or context.
-
C.
genderImplication
Indicates that one entity’s gender suggests, constrains, or determines the possible or likely gender of another entity.
-
D.
genderCategories
chosen
Indicates the classification of an entity into one or more gender-related categories or identities.
-
E.
genderUsage
Indicates how a particular gender is applied, referenced, or treated within a given context or system.
- 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_69d806a938b8819097ec43a2229fc7f9 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbaf0c177081909178dec61b09c278 |
completed | April 12, 2026, 2:41 p.m. |
| PD | Predicate disambiguation | batch_69d9a03dcd0c8190a8927eb4eaad1c45 |
completed | April 11, 2026, 1:13 a.m. |
Created at: April 9, 2026, 9:41 p.m.