Triple

T5043596
Position Surface form Disambiguated ID Type / Status
Subject Terror in a Texas Town E113605 entity
Predicate producer P490 FINISHED
Object Frank N. Seltzer E492420 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: Frank N. Seltzer | Statement: [Terror in a Texas Town, producer, Frank N. Seltzer]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Frank N. Seltzer
Context triple: [Terror in a Texas Town, producer, Frank N. Seltzer]
  • A. Frank N. Seltzer chosen
    Frank N. Seltzer was a film producer active in mid-20th-century American cinema, known for working on genre pictures including Westerns.
  • B. Larry Brezner
    Larry Brezner was an American film producer and talent manager known for producing popular comedies such as "Good Morning, Vietnam," "The 'Burbs," and "Ride Along."
  • C. Edwin Schlossberg
    Edwin Schlossberg is an American designer, artist, and author known for his innovative work in interactive museum and exhibition design.
  • D. Barry Munitz
    Barry Munitz is an American academic administrator and former university system leader best known for serving as chancellor of the California State University and later heading the J. Paul Getty Trust.
  • E. Irving Gertz
    Irving Gertz was an American film composer known for scoring mid-20th-century Hollywood movies, particularly war and science fiction 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_69bd44391fc48190a311ce9c826c209b completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd73fc04f08190aba851fa0192d0fb completed March 20, 2026, 4:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69beba5ec4308190aff8b1c4e494e0e2 completed March 21, 2026, 3:33 p.m.
Created at: March 20, 2026, 1:37 p.m.