Triple

T18739863
Position Surface form Disambiguated ID Type / Status
Subject Kennedy Otieno E458259 entity
Predicate familyName P18 FINISHED
Object Otieno
Otieno is a Kenyan surname commonly associated with the Luo ethnic community and borne by various notable figures in sports, politics, and public life.
E1340197 NE FINISHED

How this triple was built (4 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: Otieno | Statement: [Kennedy Otieno, familyName, Otieno]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Otieno
Context triple: [Kennedy Otieno, familyName, Otieno]
  • A. Ota
    Ota is a historically significant Awori town in southwestern Nigeria that has grown into a major industrial and educational hub.
  • B. Ota
    Ōta is a large ward in southern Tokyo, Japan, known for Haneda Airport, residential neighborhoods, and a mix of industrial and commercial areas.
  • C. Ota
    Ōta is a major industrial city in Japan’s northern Kantō region, known especially for its automotive manufacturing, including the headquarters and main plants of Subaru.
  • D. Ota
    Ota is a civil parish in Portugal, known for its location within the municipality of Alenquer in the Lisbon District.
  • E. Ota
    Ota is a small commune in the Corse-du-Sud department of Corsica, France, known for its dramatic coastal and mountain scenery and proximity to the UNESCO-listed Gulf of Porto.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Otieno
Triple: [Kennedy Otieno, familyName, Otieno]
Generated description
Otieno is a Kenyan surname commonly associated with the Luo ethnic community and borne by various notable figures in sports, politics, and public life.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Otieno
Target entity description: Otieno is a Kenyan surname commonly associated with the Luo ethnic community and borne by various notable figures in sports, politics, and public life.
  • A. Ota
    Ota is a historically significant Awori town in southwestern Nigeria that has grown into a major industrial and educational hub.
  • B. Ota
    Ōta is a large ward in southern Tokyo, Japan, known for Haneda Airport, residential neighborhoods, and a mix of industrial and commercial areas.
  • C. Ota
    Ōta is a major industrial city in Japan’s northern Kantō region, known especially for its automotive manufacturing, including the headquarters and main plants of Subaru.
  • D. Ota
    Ota is a civil parish in Portugal, known for its location within the municipality of Alenquer in the Lisbon District.
  • E. Ota
    Ota is a small commune in the Corse-du-Sud department of Corsica, France, known for its dramatic coastal and mountain scenery and proximity to the UNESCO-listed Gulf of Porto.
  • F. None of above. chosen

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_69d8d394dc308190b6725073f5db324c completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e5768d6a208190817abe904fab433b completed April 20, 2026, 12:42 a.m.
NED1 Entity disambiguation (via context triple) batch_6a053263e33c8190be79ec131d15c3fa completed May 14, 2026, 2:24 a.m.
NEDg Description generation batch_6a053410a8188190992d9fa6130ab372 completed May 14, 2026, 2:31 a.m.
NED2 Entity disambiguation (via description) batch_6a05349009e481909fb757415468aa70 completed May 14, 2026, 2:33 a.m.
Created at: April 10, 2026, 11:51 a.m.