Triple
T35449638
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baksa Kembang dance |
E1024591
|
entity |
| Predicate | typicalGenderOfDancers |
P206989
|
FINISHED |
| Object | female |
—
|
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: female | Statement: [Baksa Kembang dance, typicalGenderOfDancers, female]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalGenderOfDancers Context triple: [Baksa Kembang dance, typicalGenderOfDancers, female]
-
A.
typicalNumberOfDancers
Indicates the usual or standard number of dancers involved in a particular dance, performance, or context.
-
B.
hasPerformerGender
Indicates that an action, event, or performance is associated with the gender of the performer who carries it out.
-
C.
genderOfMembers
Indicates the gender or genders associated with the members of a group or organization.
-
D.
numberOfWomenDancers
Indicates the count of women who are performing as dancers in a given context or event.
-
E.
numberOfMenDancers
Indicates the count of male individuals performing as dancers in a given context or event.
- F. None of above. chosen
Provenance (4 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_69f76df8089481909f0018266ee881b7 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a037c92f03c8190ae2751270b195423 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0324d08190ac5b610cc0f6a38c |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c82179081908325a59b8539b3a8 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:04 p.m.