Triple

T14750441
Position Surface form Disambiguated ID Type / Status
Subject Giovanni Vailati E346586 entity
Predicate placeOfBirth P1 FINISHED
Object Crema E585005 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: Crema | Statement: [Giovanni Vailati, placeOfBirth, Crema]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Crema
Context triple: [Giovanni Vailati, placeOfBirth, Crema]
  • A. Crema chosen
    Crema is a historic town in the Lombardy region of northern Italy, known for its medieval architecture and cultural heritage.
  • B. Cappachino
    Cappachino is an alias of Cappadonna, an American rapper best known for his longtime affiliation with the Wu-Tang Clan.
  • C. Latte Pronto
    Latte Pronto is the central protagonist of the work "Fool's Paradise," around whom the story's main events and conflicts revolve.
  • D. Cafiero
    Cafiero is an Italian surname most notably associated with Carlo Cafiero, a prominent 19th-century anarchist and socialist activist.
  • E. Café au Lait
    Café au Lait is one of the short, conversational vignettes in Jim Jarmusch’s film "Coffee and Cigarettes," featuring characters chatting over coffee in a minimalist, black-and-white setting.
  • 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_69d822e6f1c88190bc494d491a907114 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69dec7d2e1748190b16ede681fe52872 completed April 14, 2026, 11:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69fdfb9a56a08190b6a178cd930a072d completed May 8, 2026, 3:04 p.m.
Created at: April 10, 2026, 1:30 a.m.