Triple

T30229667
Position Surface form Disambiguated ID Type / Status
Subject Kenya School of Law E768591 entity
Predicate governedBy P46 FINISHED
Object Kenya School of Law Act
The Kenya School of Law Act is a Kenyan statute that establishes and regulates the Kenya School of Law, outlining its mandate, governance structure, and functions in professional legal education and training.
E1905669 NE 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: Kenya School of Law Act | Statement: [Kenya School of Law, governedBy, Kenya School of Law Act]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Kenya School of Law Act
Triple: [Kenya School of Law, governedBy, Kenya School of Law Act]
Generated description
The Kenya School of Law Act is a Kenyan statute that establishes and regulates the Kenya School of Law, outlining its mandate, governance structure, and functions in professional legal education and training.

Provenance (5 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_69f2248108208190be60bf1af343ce70 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f6802486ac8190a936df82f2988383 completed May 2, 2026, 10:52 p.m.
NED1 Entity disambiguation (via context triple) batch_6a276444b9ac8190a969266f4dcc0c3d completed June 9, 2026, 12:54 a.m.
NEDg Description generation batch_6a2765040f8c8190a86ae1b5aa37d80b completed June 9, 2026, 12:57 a.m.
NED2 Entity disambiguation (via description) batch_6a27661767f081909e0291186c5d6778 completed June 9, 2026, 1:02 a.m.
Created at: April 29, 2026, 7:36 p.m.