Triple

T7354100
Position Surface form Disambiguated ID Type / Status
Subject The Marrying Kind E169578 entity
Predicate castMember P1668 FINISHED
Object Aldo Ray E686359 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: Aldo Ray | Statement: [The Marrying Kind, castMember, Aldo Ray]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Aldo Ray
Context triple: [The Marrying Kind, castMember, Aldo Ray]
  • A. Aldo Ray chosen
    Aldo Ray was an American film actor known for his tough-guy roles and distinctive raspy voice in numerous Hollywood movies of the 1950s and 1960s.
  • B. Robert Ryan
    Robert Ryan was an American actor renowned for his intense portrayals of tough, complex characters in mid-20th-century film noir and drama.
  • C. Robert Parrish
    Robert Parrish was an American film editor and director, as well as a former child actor, known for his work on several classic Hollywood films.
  • D. Leon Errol
    Leon Errol was an Australian-born American comedian and character actor best known for his rubber-legged physical comedy and prolific work in vaudeville, Broadway, and Hollywood films of the early 20th century.
  • E. Richard Basehart
    Richard Basehart was an American actor known for his versatile film and television roles, including performances in "La Strada" and the TV series "Voyage to the Bottom of the Sea."
  • 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_69c68a59f2288190877ca15c19b1e822 completed March 27, 2026, 1:47 p.m.
NER Named-entity recognition batch_69c6f10e71fc81909307ca39a61142d3 completed March 27, 2026, 9:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8c7adaf3c819095214864b91316f6 completed March 29, 2026, 6:33 a.m.
Created at: March 27, 2026, 3:05 p.m.