Triple

T8860874
Position Surface form Disambiguated ID Type / Status
Subject Al Martino E210882 entity
Predicate associatedAct P37 FINISHED
Object Mario Lanza E270889 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: Mario Lanza | Statement: [Al Martino, associatedAct, Mario Lanza]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mario Lanza
Context triple: [Al Martino, associatedAct, Mario Lanza]
  • A. Mario Lanza chosen
    Mario Lanza was a popular mid-20th-century American tenor and film star known for bringing operatic singing to mainstream audiences through his recordings and Hollywood movies.
  • B. Mel Ferrer
    Mel Ferrer was an American actor, director, and producer known for his work in classic Hollywood films and his marriage to Audrey Hepburn.
  • C. Antonino Martino Sinatra
    Antonino Martino Sinatra was an Italian-born immigrant to the United States best known as the father of legendary singer and actor Frank Sinatra.
  • D. Tony Musante
    Tony Musante was an American actor known for his intense, often gritty performances in film and television, including notable roles in works like "The Incident" and the TV series "Toma."
  • E. Troy Donahue
    Troy Donahue was an American actor and teen idol of the late 1950s and early 1960s, best known for his roles in romantic dramas and beach-themed films.
  • 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_69ca838bbddc8190ab546d737e5d350f completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc60e860888190a8a8702377db949e completed April 1, 2026, 12:03 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfa0b94f5481909902b5fa405a502f completed April 3, 2026, 11:12 a.m.
Created at: March 30, 2026, 6:50 p.m.