Triple
T8585920
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gaddafi National Mosque |
E203304
|
entity |
| Predicate | genderSeparation |
P25470
|
FINISHED |
| Object | separate prayer areas for men and 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: separate prayer areas for men and women | Statement: [Gaddafi National Mosque, genderSeparation, separate prayer areas for men and women]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genderSeparation Context triple: [Gaddafi National Mosque, genderSeparation, separate prayer areas for men and women]
-
A.
genderDivision
chosen
Indicates a relationship where roles, responsibilities, or categories are separated or distinguished based on gender.
-
B.
genderCategories
Indicates the classification of an entity into one or more gender-related categories or identities.
-
C.
genderConfiguration
Indicates how the genders of the involved entities are arranged or combined within a particular relationship or context.
-
D.
genderSignificance
Indicates the relevance or impact that an entity’s gender has within a particular context, relationship, or interpretation.
-
E.
sexOrGender
Indicates that one entity has a specified biological sex or socially constructed gender identity.
- 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_69ca8329bb7c8190a63c643730839103 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cc46c5e8888190b721e791c449b0df |
completed | March 31, 2026, 10:12 p.m. |
| PD | Predicate disambiguation | batch_69cc454504448190aaad2af8b17357cd |
completed | March 31, 2026, 10:05 p.m. |
Created at: March 30, 2026, 6:22 p.m.