Triple

T6200416
Position Surface form Disambiguated ID Type / Status
Subject Nik E138614 entity
Predicate teammate P2649 FINISHED
Object Kaz
Kaz is a person known for working closely with Nik as a teammate, likely in a collaborative or competitive setting such as sports, gaming, or a professional project.
 E575844 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: Kaz | Statement: [Nik, teammate, Kaz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kaz
Context triple: [Nik, teammate, Kaz]
  • A. Kaz
    Kaz is one of the futuristic, computer-generated Spheriks characters that served as an official mascot for the 2002 FIFA World Cup in South Korea and Japan.
  • B. KAZ
    KAZ is the three-letter ISO 3166-1 alpha-3 country code assigned to Kazakhstan for international standardization and identification.
  • C. Kas
    Kas is the historical name of Shahrisabz, an ancient city in southern Uzbekistan renowned as the birthplace of Timur (Tamerlane) and for its significant Timurid-era architectural monuments.
  • D. Katsuya
    Katsuya is a Japanese given name commonly used for males.
  • E. Kai
    Kai is the eldest granddaughter of former U.S. President Donald Trump and the daughter of Donald Trump Jr.
  • 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: Kaz
Triple: [Nik, teammate, Kaz]
Generated description
Kaz is a person known for working closely with Nik as a teammate, likely in a collaborative or competitive setting such as sports, gaming, or a professional project.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kaz
Target entity description: Kaz is a person known for working closely with Nik as a teammate, likely in a collaborative or competitive setting such as sports, gaming, or a professional project.
  • A. Kaz
    Kaz is one of the futuristic, computer-generated Spheriks characters that served as an official mascot for the 2002 FIFA World Cup in South Korea and Japan.
  • B. KAZ
    KAZ is the three-letter ISO 3166-1 alpha-3 country code assigned to Kazakhstan for international standardization and identification.
  • C. Kas
    Kas is the historical name of Shahrisabz, an ancient city in southern Uzbekistan renowned as the birthplace of Timur (Tamerlane) and for its significant Timurid-era architectural monuments.
  • D. Katsuya
    Katsuya is a Japanese given name commonly used for males.
  • E. Kai
    Kai is the eldest granddaughter of former U.S. President Donald Trump and the daughter of Donald Trump Jr.
  • 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_69c008acbea48190991c6b834bb45d65 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c062547cd48190a2715537b961262e completed March 22, 2026, 9:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69c16f366cfc81909cca73677268821a completed March 23, 2026, 4:49 p.m.
NEDg Description generation batch_69c1e375c5948190ad166089e866694a completed March 24, 2026, 1:05 a.m.
NED2 Entity disambiguation (via description) batch_69c1e43fa8348190a2247996d88b5011 completed March 24, 2026, 1:09 a.m.
Created at: March 22, 2026, 4:20 p.m.