Triple

T15311805
Position Surface form Disambiguated ID Type / Status
Subject How to Lose a Guy in 10 Days E366055 entity
Predicate producer P490 FINISHED
Object Lynda Obst E159259 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: Lynda Obst | Statement: [How to Lose a Guy in 10 Days, producer, Lynda Obst]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lynda Obst
Context triple: [How to Lose a Guy in 10 Days, producer, Lynda Obst]
  • A. Lynda Obst chosen
    Lynda Obst is an American film producer and author known for her work on major Hollywood films, including the science fiction epic "Interstellar."
  • B. Lynda Bernhard
    Lynda Bernhard is known as the wife of American film producer Harvey Bernhard.
  • C. Lucinda Jenney
    Lucinda Jenney is an American character actress known for her versatile supporting roles in films and television since the 1980s.
  • D. Lynda Petty
    Lynda Petty was the longtime wife of NASCAR legend Richard Petty and a prominent figure in the racing community known for her charitable work and support of the Petty family’s motorsports legacy.
  • E. Lisa Reisert
    Lisa Reisert is the resourceful and determined protagonist of the thriller film "Red Eye," who becomes entangled in a high-stakes assassination plot during a red-eye flight.
  • 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_69d85a113ee881908e297a1d38dd79fa completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03cd2d5a88190aead748920f93d47 completed April 16, 2026, 1:35 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff01e70a308190a7d6b91178c39bd3 completed May 9, 2026, 9:44 a.m.
Created at: April 10, 2026, 3:16 a.m.