Triple

T25425015
Position Surface form Disambiguated ID Type / Status
Subject Judiciary of Kenya E637095 entity
Predicate hasComponent P35 FINISHED
Object Kadhi’s Courts of Kenya
Kadhi’s Courts of Kenya are specialized Islamic law courts that handle matters such as marriage, divorce, and inheritance for consenting Muslims within Kenya’s judicial system.
 E1678931 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: Kadhi’s Courts of Kenya | Statement: [Judiciary of Kenya, hasComponent, Kadhi’s Courts of Kenya]
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: Kadhi’s Courts of Kenya
Triple: [Judiciary of Kenya, hasComponent, Kadhi’s Courts of Kenya]
Generated description
Kadhi’s Courts of Kenya are specialized Islamic law courts that handle matters such as marriage, divorce, and inheritance for consenting Muslims within Kenya’s judicial system.

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_69e75db58a1c8190891b9ff7c2f8414e completed April 21, 2026, 11:21 a.m.
NER Named-entity recognition batch_69f5f6bf25c881909f049d5393927bfb completed May 2, 2026, 1:06 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10899c570881908096628a9552cef9 completed May 22, 2026, 4:51 p.m.
NEDg Description generation batch_6a108a66ebfc8190843d591e9ab47493 completed May 22, 2026, 4:55 p.m.
NED2 Entity disambiguation (via description) batch_6a108b1c1e888190b3fe80f1da6a4be5 completed May 22, 2026, 4:58 p.m.
Created at: April 21, 2026, 1:57 p.m.