Triple

T21952112
Position Surface form Disambiguated ID Type / Status
Subject Jeremiah Johnson E542093 entity
Predicate starring P1507 FINISHED
Object Joaquin Martinez NE NERFINISHED

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: Joaquin Martinez | Statement: [Jeremiah Johnson, starring, Joaquin Martinez]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Joaquin Martinez
Context triple: [Jeremiah Johnson, starring, Joaquin Martinez]
  • A. Joaquín Martínez chosen
    Joaquín Martínez was a Mexican actor known for his character roles in American Western films and television during the 1960s and 1970s.
  • B. Joaquin Miguel Elizalde
    Joaquin Miguel Elizalde was a prominent Filipino diplomat and businessman who became the first Philippine ambassador to the United States following the country’s independence.
  • C. Roberto Muzquiz
    Roberto Muzquiz is the child of Rosaura De la Garza, a member of her immediate family.
  • D. Óscar Magaña
    Óscar Magaña is a notable individual who carries the surname Magaña, recognized for achievements that distinguish him among bearers of the name.
  • E. Isaac Garza
    Isaac Garza was a Mexican businessman and industrialist best known as a founding figure behind the conglomerate that evolved into FEMSA, one of Latin America’s largest beverage and retail companies.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0c47ef0e48190a50e1bcc43f4b3fd completed April 16, 2026, 11:14 a.m.
NER Named-entity recognition batch_69f1243c84d4819097f5a93b128f024b completed April 28, 2026, 9:18 p.m.
Created at: April 16, 2026, 7:58 p.m.