Triple
T30181938
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ugandan Muslims |
E767223
|
entity |
| Predicate | hasWomenOrganizations |
P32258
|
FINISHED |
| Object | Muslim women associations in Uganda |
—
|
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: Muslim women associations in Uganda | Statement: [Ugandan Muslims, hasWomenOrganizations, Muslim women associations in Uganda]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWomenOrganizations Context triple: [Ugandan Muslims, hasWomenOrganizations, Muslim women associations in Uganda]
-
A.
hasWomenOrganization
chosen
Indicates that an entity is associated with, contains, or is part of an organization specifically for women.
-
B.
hadWomenOrganization
Indicates that an entity was associated with or involved in an organization focused on women or women’s issues.
-
C.
hasOrganizations
Indicates that an entity is associated with, linked to, or includes one or more organizations.
-
D.
hadFemaleMembers
Indicates that the subject group or organization included one or more female individuals among its members.
-
E.
womenStatus
Indicates the social, legal, economic, or cultural position or condition assigned to women 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_69f2247cc3d88190811dec3face94bf5 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69fd0b92f42881908cd77e3f058adcc2 |
completed | May 7, 2026, 10 p.m. |
| PD | Predicate disambiguation | batch_69fd0a3d68d4819094d92040f7c48d7c |
completed | May 7, 2026, 9:55 p.m. |
Created at: April 29, 2026, 7:26 p.m.