Triple
T23736403
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Consultative Assembly of Saudi Arabia |
E586551
|
entity |
| Predicate | hasWomenMembers |
P105258
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Consultative Assembly of Saudi Arabia, hasWomenMembers, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWomenMembers Context triple: [Consultative Assembly of Saudi Arabia, hasWomenMembers, yes]
-
A.
hadFemaleMembers
chosen
Indicates that the subject group or organization included one or more female individuals among its members.
-
B.
hasWomenOrganization
Indicates that an entity is associated with, contains, or is part of an organization specifically for women.
-
C.
hadWomenOrganization
Indicates that an entity was associated with or involved in an organization focused on women or women’s issues.
-
D.
genderOfMembers
Indicates the gender or genders associated with the members of a group or organization.
-
E.
memberCountFemale
Indicates the number of female members associated with a given group or entity.
- 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_69e24907dc9c8190be074c9c96a0ec2d |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1bad28fa481909d7a6a6e98a7b0a5 |
completed | April 29, 2026, 8:01 a.m. |
| PD | Predicate disambiguation | batch_69f155f012808190a4b1cbc155558ade |
completed | April 29, 2026, 12:50 a.m. |
Created at: April 17, 2026, 7:10 p.m.