Triple

T6402949
Position Surface form Disambiguated ID Type / Status
Subject Jack Palance E144106 entity
Predicate alsoKnownAs P39 FINISHED
Object Walter Jack Palance E144106 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: Walter Jack Palance | Statement: [Jack Palance, alsoKnownAs, Walter Jack Palance]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Walter Jack Palance
Context triple: [Jack Palance, alsoKnownAs, Walter Jack Palance]
  • A. Jack Palance chosen
    Jack Palance was an American actor known for his intense, rugged screen presence and memorable roles in films such as "Shane" and "City Slickers."
  • B. Efrem Zimbalist
    Efrem Zimbalist was a renowned Russian-American violinist, composer, and influential pedagogue of the 20th century.
  • C. Frank Giustra
    Frank Giustra is a Canadian businessman and philanthropist best known as the founder of Lionsgate Entertainment and for his extensive work in the mining and film industries.
  • D. Peter Neal
    Peter Neal is an American lawyer known for being married to Naomi Biden, the granddaughter of U.S. President Joe Biden.
  • E. Dan Duryea
    Dan Duryea was an American character actor best known for his distinctive portrayals of sneering villains and tough guys in film noir and classic Hollywood movies of the 1940s and 1950s.
  • 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_69c008dc56fc81908d43ffcc11d73bdd completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c068af3f448190a94ecd5109e9e8e4 completed March 22, 2026, 10:09 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7616755d48190b285ff66ccd8bf3a completed March 28, 2026, 5:04 a.m.
Created at: March 22, 2026, 4:35 p.m.