Triple
T9889477
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | qipao |
E181416
|
entity |
| Predicate | genderTypicallyWornBy |
P34349
|
FINISHED |
| Object | women |
—
|
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: women | Statement: [qipao, genderTypicallyWornBy, women]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genderTypicallyWornBy Context triple: [qipao, genderTypicallyWornBy, women]
-
A.
isGenderSpecificCategory
Indicates that the category applies specifically to one gender rather than being gender-neutral.
-
B.
namedForGender
Indicates that one entity is named in a way that reflects or is derived from a particular gender or gender-related characteristic of another entity.
-
C.
genderTarget
Indicates that an action, message, or effect is specifically directed toward entities of a particular gender.
-
D.
typicallyWornBy
Indicates that something (such as an item or garment) is most commonly or characteristically worn by a particular type of person or group.
-
E.
hasTypicalGenderAssociation
chosen
Indicates that one entity is commonly or culturally associated with a particular gender more than with other genders.
- 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_69ca8283a6708190801af7a25a7ebb9f |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cdb47be2988190811a99dc56ae542a |
completed | April 2, 2026, 12:12 a.m. |
| PD | Predicate disambiguation | batch_69cd1d810ed48190a252b70e9390c8f3 |
completed | April 1, 2026, 1:28 p.m. |
Created at: March 30, 2026, 8:39 p.m.