Triple

T11760573
Position Surface form Disambiguated ID Type / Status
Subject Duck, You Sucker! E279643 entity
Predicate editor P1954 FINISHED
Object Nino Baragli E376954 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: Nino Baragli | Statement: [Duck, You Sucker!, editor, Nino Baragli]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nino Baragli
Context triple: [Duck, You Sucker!, editor, Nino Baragli]
  • A. Nino Baragli chosen
    Nino Baragli was an Italian film editor renowned for his work on many classic films, particularly in collaboration with directors like Sergio Leone and Pier Paolo Pasolini.
  • B. Paolo Ajroldi
    Paolo Ajroldi is an advertising executive best known for co-founding the global marketing and communications agency TBWA Worldwide.
  • C. Giovanni Molari
    Giovanni Molari is an Italian academic and engineer who serves as rector of the historic University of Bologna.
  • D. Antonio Frova
    Antonio Frova was an Italian archaeologist best known for unearthing the Pilate Stone, a key inscription confirming the historical existence of Pontius Pilate.
  • E. Filippo Barigioni
    Filippo Barigioni was an Italian Baroque architect and sculptor active in Rome in the early 18th century, known for his work on churches, fountains, and urban spaces.
  • 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_69d6ab01038c819080714901502c84fc completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8a52386708190b744746a2db37495 completed April 10, 2026, 7:22 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffa92635b08190a333702bec5b94e9 completed May 9, 2026, 9:37 p.m.
Created at: April 8, 2026, 9:41 p.m.