Triple

T11232103
Position Surface form Disambiguated ID Type / Status
Subject FIFA World Cup mascots series E265846 entity
Predicate hasMascot P52 FINISHED
Object Kaz E133197 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: Kaz | Statement: [FIFA World Cup mascots series, hasMascot, Kaz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kaz
Context triple: [FIFA World Cup mascots series, hasMascot, Kaz]
  • A. Kaz chosen
    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 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.
  • C. Kaz
    Kaz is a central protagonist in the Disney XD series "Mighty Med," known as a comic book fan who becomes a sidekick and caretaker to real-life superheroes.
  • D. KAZ
    KAZ is the three-letter ISO 3166-1 alpha-3 country code assigned to Kazakhstan for international standardization and identification.
  • E. Kazuno
    Kazuno is a city in northern Japan known for its hot springs, traditional festivals, and mountainous rural scenery.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d6aac656d48190b275efaa7d6074ee completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e9026e1c81909456ac946bbba972 completed April 9, 2026, 5:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69e4ad49b5cc8190b99cb2cd8de72109 completed April 19, 2026, 10:24 a.m.
Created at: April 8, 2026, 9:30 p.m.