Triple

T35857550
Position Surface form Disambiguated ID Type / Status
Subject United Democratic Alliance E1036550 entity
Predicate notableMember P10 FINISHED
Object Kipchumba Murkomen
Kipchumba Murkomen is a Kenyan politician and lawyer who has served as a senator and cabinet secretary, known for his influential role within Kenya’s ruling political coalitions.
E2159370 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: Kipchumba Murkomen | Statement: [United Democratic Alliance, notableMember, Kipchumba Murkomen]
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: Kipchumba Murkomen
Triple: [United Democratic Alliance, notableMember, Kipchumba Murkomen]
Generated description
Kipchumba Murkomen is a Kenyan politician and lawyer who has served as a senator and cabinet secretary, known for his influential role within Kenya’s ruling political 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_69f76e1b4aa481909630373171eb5ec6 completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f7a97378f8819081b6c098052767de completed May 3, 2026, 8 p.m.
NED1 Entity disambiguation (via context triple) batch_6a38a4e6b3f0819094f57043c295ac91 completed June 22, 2026, 2:58 a.m.
NEDg Description generation batch_6a38a5618be48190893b3e8202847748 completed June 22, 2026, 3 a.m.
NED2 Entity disambiguation (via description) batch_6a38a5fa291c81909955855947ef19d5 completed June 22, 2026, 3:03 a.m.
Created at: May 3, 2026, 4:06 p.m.