Triple

T20141114
Position Surface form Disambiguated ID Type / Status
Subject Things Heard & Seen E491165 entity
Predicate editedBy P1954 FINISHED
Object Patricia Rommel 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: Patricia Rommel | Statement: [Things Heard & Seen, editedBy, Patricia Rommel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Patricia Rommel
Context triple: [Things Heard & Seen, editedBy, Patricia Rommel]
  • A. Patricia Rommel chosen
    Patricia Rommel is a German film editor known for her work on numerous international films, including acclaimed European and Hollywood productions.
  • B. Patricia Ziegler
    Patricia Ziegler is an American entrepreneur and co-founder of the Banana Republic clothing brand, known for transforming it from a small safari-themed shop into a popular retail chain.
  • C. Patricia Volk
    Patricia Volk is an American author and essayist known for her witty memoirs and fiction, often drawing on her New York City upbringing and family life.
  • D. Patricia Haas
    Patricia Haas is known as the wife of American astronaut and Apollo 9 commander James McDivitt.
  • E. Patricia Knop
    Patricia Knop was an American screenwriter and playwright best known for co-writing the film "9½ Weeks" and contributing to various stage and film projects.
  • 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_69da6265f8f0819080b29c752a574088 completed April 11, 2026, 3:01 p.m.
NER Named-entity recognition batch_69e6679b179c8190a9511df8ed82098a completed April 20, 2026, 5:51 p.m.
Created at: April 11, 2026, 11:32 p.m.