Triple

T2358884
Position Surface form Disambiguated ID Type / Status
Subject Luo people E47222 entity
Predicate educationImpact P10669 FINISHED
Object high representation in Kenyan professional sectors 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: high representation in Kenyan professional sectors | Statement: [Luo people, educationImpact, high representation in Kenyan professional sectors]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: educationImpact
Context triple: [Luo people, educationImpact, high representation in Kenyan professional sectors]
  • A. educationalImpact chosen
    Indicates the effect or influence that one entity has on the learning, knowledge, or educational outcomes of another.
  • B. educationRight
    Indicates that an entity holds a right or entitlement to receive education or educational opportunities.
  • C. educationIndicator
    Indicates that there is a measure or metric reflecting some aspect of educational status, performance, or outcomes associated with the entities.
  • D. educationPolicy
    Indicates a relationship where an authority or entity establishes, governs, or influences rules, strategies, or frameworks guiding an education system or educational practices.
  • E. educationTrend
    Indicates a pattern or direction of change over time in some aspect of education, such as participation, attainment, or performance.
  • 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_69a88a1a4a6081908645b0f2914521ab completed March 4, 2026, 7:38 p.m.
NER Named-entity recognition batch_69abc720b9048190a5d3b19e5e1f373a completed March 7, 2026, 6:35 a.m.
PD Predicate disambiguation batch_69abc599b92c819093d9e15d4437705d completed March 7, 2026, 6:28 a.m.
Created at: March 4, 2026, 7:55 p.m.