Triple

T7950646
Position Surface form Disambiguated ID Type / Status
Subject Any Given Sunday E184604 entity
Predicate producer P490 FINISHED
Object Lauren Shuler Donner E166986 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: Lauren Shuler Donner | Statement: [Any Given Sunday, producer, Lauren Shuler Donner]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lauren Shuler Donner
Context triple: [Any Given Sunday, producer, Lauren Shuler Donner]
  • A. Lauren Shuler Donner chosen
    Lauren Shuler Donner is an American film producer best known for her work on major studio films including the X-Men franchise and other popular Hollywood features.
  • B. Natalie Desselle
    Natalie Desselle was an American actress best known for her comedic roles in film and television, including her memorable performance in the 1997 adaptation of "Cinderella."
  • C. Kirsten Nelson
    Kirsten Nelson is an American actress best known for her role as police chief Karen Vick on the television series "Psych."
  • D. Heather Matarazzo
    Heather Matarazzo is an American actress best known for her character roles in films like "Welcome to the Dollhouse" and "The Princess Diaries."
  • E. Ari Wegner
    Ari Wegner is an acclaimed Australian cinematographer known for her visually striking work on films such as "The Power of the Dog."
  • 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_69ca8292cba881908a64427b938dac47 completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb3b5b7450819091e4e6f21e9d832d completed March 31, 2026, 3:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69cc63af95fc81908608dcb0eb7e9893 completed April 1, 2026, 12:15 a.m.
Created at: March 30, 2026, 5:10 p.m.