Triple
T10020855
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | National Assembly of Kenya |
E200605
|
entity |
| Predicate | numberOfWomenCountyMembers |
P91738
|
FINISHED |
| Object | 47 |
—
|
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: 47 | Statement: [National Assembly of Kenya, numberOfWomenCountyMembers, 47]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfWomenCountyMembers Context triple: [National Assembly of Kenya, numberOfWomenCountyMembers, 47]
-
A.
numberOfDistrictMembers
Indicates the relationship that specifies how many members are associated with a given district.
-
B.
numberOfCommitteeMembers
Indicates the total count of individuals who are members of a given committee.
-
C.
numberOfDemocraticMembers
Indicates the quantity of members in a group or body who are affiliated with or belong to the Democratic Party.
-
D.
womenRepresentationMechanism
Indicates the mechanism or process through which women’s representation or participation is ensured, structured, or facilitated in a given context.
-
E.
numberOfElectedMembers
Indicates the total count of individuals who have been formally chosen through an election to serve as members of a given body or group.
- F. None of above. chosen
Provenance (4 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_69ca831c45f08190ac1505cc15076608 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cdcd777b208190ad75eac79eec0c2f |
completed | April 2, 2026, 1:59 a.m. |
| PD | Predicate disambiguation | batch_69cd4b7cd4208190b2253583ee2f892c |
completed | April 1, 2026, 4:44 p.m. |
| PDg | Predicate description generation | batch_69cd4f8d9b888190b8067bd916dae773 |
completed | April 1, 2026, 5:02 p.m. |
Created at: March 30, 2026, 8:53 p.m.