Triple

T29659743
Position Surface form Disambiguated ID Type / Status
Subject The National Alliance (Kenya) E750372 entity
Predicate coalitionPartner P1904 FINISHED
Object Chama Cha Uzalendo
Chama Cha Uzalendo is a Kenyan political party known for its regional grassroots base and participation in national electoral coalitions.
E1880353 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: Chama Cha Uzalendo | Statement: [The National Alliance (Kenya), coalitionPartner, Chama Cha Uzalendo]
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: Chama Cha Uzalendo
Triple: [The National Alliance (Kenya), coalitionPartner, Chama Cha Uzalendo]
Generated description
Chama Cha Uzalendo is a Kenyan political party known for its regional grassroots base and participation in national electoral coalitions.

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_69f0d6226fe881908819197c9ef9ee04 completed April 28, 2026, 3:45 p.m.
NER Named-entity recognition batch_69f671bf54688190a920fed1847a1a9b completed May 2, 2026, 9:50 p.m.
NED1 Entity disambiguation (via context triple) batch_6a267eb452b88190b10573cea9644dd6 completed June 8, 2026, 8:35 a.m.
NEDg Description generation batch_6a2682fadaf48190a4d691901671b579 completed June 8, 2026, 8:53 a.m.
NED2 Entity disambiguation (via description) batch_6a268ec8f3908190a8801d62e978ac15 completed June 8, 2026, 9:43 a.m.
Created at: April 28, 2026, 6:57 p.m.