Triple
T25684047
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ambubachi Mela |
E644018
|
entity |
| Predicate | genderRelatedAspect |
P60515
|
FINISHED |
| Object | celebrates female reproductive power |
—
|
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: celebrates female reproductive power | Statement: [Ambubachi Mela, genderRelatedAspect, celebrates female reproductive power]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genderRelatedAspect Context triple: [Ambubachi Mela, genderRelatedAspect, celebrates female reproductive power]
-
A.
genderSpecificity
Indicates whether the relationship or action applies specifically to a particular gender or is gender-neutral.
-
B.
genderImplication
Indicates that one entity’s gender suggests, constrains, or determines the possible or likely gender of another entity.
-
C.
genderSignificance
chosen
Indicates the relevance or impact that an entity’s gender has within a particular context, relationship, or interpretation.
-
D.
genderCategories
Indicates the classification of an entity into one or more gender-related categories or identities.
-
E.
genderConfiguration
Indicates how the genders of the involved entities are arranged or combined within a particular relationship or context.
- 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_69e77e8046888190b07ffa58c7e2c37a |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f5fb7b3d008190ab61ce5c33893540 |
completed | May 2, 2026, 1:26 p.m. |
| PD | Predicate disambiguation | batch_69f5afec3e94819080d9ba86cf8c866e |
completed | May 2, 2026, 8:03 a.m. |
Created at: April 21, 2026, 8:05 p.m.