Triple

T6649792
Position Surface form Disambiguated ID Type / Status
Subject Kopa Trophy E150790 entity
Predicate notableWinner P2766 FINISHED
Object Gavi
Gavi is a Spanish professional footballer, known as a highly talented young midfielder for FC Barcelona and the Spain national team.
 E609743 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: Gavi | Statement: [Kopa Trophy, notableWinner, Gavi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Gavi
Context triple: [Kopa Trophy, notableWinner, Gavi]
  • A. Gavi
    Gavi is a small, uninhabited island in Italy’s Pontine archipelago, known for its rugged coastline and protected natural environment.
  • B. Nasar
    Nasar is a surname most notably associated with Sylvia Nasar, the economist and author of "A Beautiful Mind."
  • C. Colta
    Colta is a rural canton in Ecuador’s central highlands known for its indigenous communities, colonial history, and scenic Andean landscapes.
  • D. Beni-Amer
    The Beni-Amer are a pastoralist ethnic group of mixed Beja and Tigre heritage living primarily in eastern Sudan and western Eritrea.
  • E. Fondo Sur
    Fondo Sur is the southern stand of Madrid’s former Estadio Vicente Calderón, historically occupied by some of Atlético de Madrid’s most passionate supporters.
  • 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: Gavi
Triple: [Kopa Trophy, notableWinner, Gavi]
Generated description
Gavi is a Spanish professional footballer, known as a highly talented young midfielder for FC Barcelona and the Spain national team.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Gavi
Target entity description: Gavi is a Spanish professional footballer, known as a highly talented young midfielder for FC Barcelona and the Spain national team.
  • A. Gavi
    Gavi is a small, uninhabited island in Italy’s Pontine archipelago, known for its rugged coastline and protected natural environment.
  • B. Nasar
    Nasar is a surname most notably associated with Sylvia Nasar, the economist and author of "A Beautiful Mind."
  • C. Colta
    Colta is a rural canton in Ecuador’s central highlands known for its indigenous communities, colonial history, and scenic Andean landscapes.
  • D. Beni-Amer
    The Beni-Amer are a pastoralist ethnic group of mixed Beja and Tigre heritage living primarily in eastern Sudan and western Eritrea.
  • E. Fondo Sur
    Fondo Sur is the southern stand of Madrid’s former Estadio Vicente Calderón, historically occupied by some of Atlético de Madrid’s most passionate supporters.
  • 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_69c687f2c9508190a60b9aad31d3f358 completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6b04408508190a87a669b32364368 completed March 27, 2026, 4:28 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6eef91eb08190a00ae027c21b08bf completed March 27, 2026, 8:56 p.m.
NEDg Description generation batch_69c6f1ed97248190ab2253b3b2457f4f completed March 27, 2026, 9:09 p.m.
NED2 Entity disambiguation (via description) batch_69c6f2c18c0081908958b7ffeed7a787 completed March 27, 2026, 9:12 p.m.
Created at: March 27, 2026, 2:01 p.m.