Triple

T9266982
Position Surface form Disambiguated ID Type / Status
Subject Olympic Games mascots E222724 entity
Predicate notableExample P1503 FINISHED
Object Frigi
Frigi is the official mascot character created to represent and promote a specific edition of the Olympic Games.
 E788697 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: Frigi | Statement: [Olympic Games mascots, notableExample, Frigi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Frigi
Context triple: [Olympic Games mascots, notableExample, Frigi]
  • A. Frosta
    Frosta is a rural municipality and peninsula in Trøndelag county, Norway, known for its fertile farmland and historical significance as a medieval assembly site.
  • B. Frunze
    Frunze is a surname most notably associated with Mikhail Frunze, a prominent Bolshevik leader and Red Army commander during the Russian Civil War.
  • C. Blizne
    Blizne is a village in southeastern Poland best known for its historic wooden All Saints Church, a UNESCO World Heritage Site.
  • D. Frías
    Frías is a Spanish-language surname commonly found in Spain and Latin America.
  • E. Zima
    Zima is a surname most notably associated with a family of American actresses, including Yvonne Zima and her sisters Madeline and Vanessa.
  • 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: Frigi
Triple: [Olympic Games mascots, notableExample, Frigi]
Generated description
Frigi is the official mascot character created to represent and promote a specific edition of the Olympic Games.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Frigi
Target entity description: Frigi is the official mascot character created to represent and promote a specific edition of the Olympic Games.
  • A. Frosta
    Frosta is a rural municipality and peninsula in Trøndelag county, Norway, known for its fertile farmland and historical significance as a medieval assembly site.
  • B. Frunze
    Frunze is a surname most notably associated with Mikhail Frunze, a prominent Bolshevik leader and Red Army commander during the Russian Civil War.
  • C. Blizne
    Blizne is a village in southeastern Poland best known for its historic wooden All Saints Church, a UNESCO World Heritage Site.
  • D. Frías
    Frías is a Spanish-language surname commonly found in Spain and Latin America.
  • E. Zima
    Zima is a surname most notably associated with a family of American actresses, including Yvonne Zima and her sisters Madeline and Vanessa.
  • 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_69ca841f2e808190a64f4c31903a1332 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd074bac9481909419988a9e8d9bd5 completed April 1, 2026, 11:53 a.m.
NED1 Entity disambiguation (via context triple) batch_69d09c193b548190afe79d0c84fa2bd3 completed April 4, 2026, 5:05 a.m.
NEDg Description generation batch_69d09cf11e488190b61f4a61002454e6 completed April 4, 2026, 5:09 a.m.
NED2 Entity disambiguation (via description) batch_69d09e2450048190b5aa31507e54d6c8 completed April 4, 2026, 5:14 a.m.
Created at: March 30, 2026, 7:33 p.m.