Triple
T19367776
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Egyptian parliamentary election, 2015 |
E484446
|
entity |
| Predicate | womenQuota |
P45895
|
FINISHED |
| Object | reserved seats for women on party lists |
—
|
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: reserved seats for women on party lists | Statement: [Egyptian parliamentary election, 2015, womenQuota, reserved seats for women on party lists]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: womenQuota Context triple: [Egyptian parliamentary election, 2015, womenQuota, reserved seats for women on party lists]
-
A.
womenRepresentationMechanism
chosen
Indicates the mechanism or process through which women’s representation or participation is ensured, structured, or facilitated in a given context.
-
B.
womenStatus
Indicates the social, legal, economic, or cultural position or condition assigned to women within a given context or system.
-
C.
numberOfWomenCountyMembers
Indicates the count of women who are members within a given county.
-
D.
hasFemaleLeader
Indicates that the subject entity is led or governed by a woman in a primary leadership role.
-
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_69d8e8d305088190ad13571532aa454c |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e619adac8881909136c50351f3683d |
completed | April 20, 2026, 12:18 p.m. |
| PD | Predicate disambiguation | batch_69e4fd54f8e48190956e73dd8969164a |
completed | April 19, 2026, 4:05 p.m. |
Created at: April 10, 2026, 1:35 p.m.