Triple

T19338796
Position Surface form Disambiguated ID Type / Status
Subject A Guide for the Married Man E483694 entity
Predicate screenwriter P2831 FINISHED
Object Frank Tarloff 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: Frank Tarloff | Statement: [A Guide for the Married Man, screenwriter, Frank Tarloff]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Frank Tarloff
Context triple: [A Guide for the Married Man, screenwriter, Frank Tarloff]
  • A. Frank Tarloff chosen
    Frank Tarloff was an American screenwriter best known for his work in film and television comedy, including his Academy Award-winning script for "Father Goose."
  • B. Paul Lovett
    Paul Lovett is a film screenwriter best known for co-writing the action drama movie "Four Brothers."
  • C. George L. Dahl
    George L. Dahl was a prominent 20th-century American architect known for shaping much of Dallas’s skyline and major civic landmarks.
  • D. Fred M. Wilcox
    Fred M. Wilcox was an American film director best known for the science fiction classic "Forbidden Planet" and the family film "Lassie Come Home."
  • E. Alan E. Nourse
    Alan E. Nourse was an American science fiction author and physician known for works that often explored medical and social themes, including the novel "The Bladerunner."
  • 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_69d8e8d244f8819080eb1f3491300db2 completed April 10, 2026, 12:10 p.m.
NER Named-entity recognition batch_69e6185538d48190ae4fa3ac0b0ca182 completed April 20, 2026, 12:13 p.m.
Created at: April 10, 2026, 1:33 p.m.