Triple

T9788806
Position Surface form Disambiguated ID Type / Status
Subject Inside Out 2 E237554 entity
Predicate producer P490 FINISHED
Object Mark Nielsen E238793 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: Mark Nielsen | Statement: [Inside Out 2, producer, Mark Nielsen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mark Nielsen
Context triple: [Inside Out 2, producer, Mark Nielsen]
  • A. Mark Nielsen chosen
    Mark Nielsen is a film producer best known for his work on Pixar's animated feature "Toy Story 4."
  • B. Victor Kilian
    Victor Kilian was an American character actor known for his prolific work in film and television from the 1920s through the 1970s.
  • C. Jesse James Garrett
    Jesse James Garrett is an American user experience designer and author best known for coining the term AJAX and influencing modern web application design.
  • D. Bill Buxton
    Bill Buxton is a pioneering computer scientist and designer known for his influential work in human-computer interaction, input technologies, and user experience design.
  • E. Kim Milton Nielsen
    Kim Milton Nielsen is a retired Danish football referee known for officiating numerous high-profile international matches, including World Cup and UEFA Champions League games.
  • 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_69ca84da927881909bda80caecad6010 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cda2131164819099e8644e40a3cab6 completed April 1, 2026, 10:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1c427cb2c81909fce8e1958ab3282 completed April 5, 2026, 2:08 a.m.
Created at: March 30, 2026, 8:27 p.m.