Triple

T6649694
Position Surface form Disambiguated ID Type / Status
Subject Juventus Women E150788 entity
Predicate chairman P377 FINISHED
Object Gianluca Ferrero E150786 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: Gianluca Ferrero | Statement: [Juventus Women, chairman, Gianluca Ferrero]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gianluca Ferrero
Context triple: [Juventus Women, chairman, Gianluca Ferrero]
  • A. Gianluca Ferrero chosen
    Gianluca Ferrero is an Italian businessman who serves as the president of Juventus Football Club.
  • B. Giovanni Goria
    Giovanni Goria was an Italian Christian Democrat politician who briefly served as Prime Minister of Italy in the late 1980s during the era of the Pentapartito coalition governments.
  • C. Attilio Pusterla
    Attilio Pusterla was an Italian-American artist known for creating the historical murals that adorn the Astoria Column in Oregon.
  • D. Matteo Bartoli
    Matteo Bartoli was an Italian linguist known for his pioneering work in dialectology and Romance linguistics, particularly the study of the Dalmatian language.
  • E. Federico Bruni
    Federico Bruni, better known as Fyodor Bruni, was a 19th-century Italian-Russian painter renowned for his large-scale historical and religious works in the academic style.
  • 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_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_69c6f79642508190a2e3810e347f2e93 completed March 27, 2026, 9:33 p.m.
Created at: March 27, 2026, 2:01 p.m.