Triple

T14657034
Position Surface form Disambiguated ID Type / Status
Subject Mr. Mom E344137 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: [Mr. Mom, producer, Lauren Shuler Donner]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lauren Shuler Donner
Context triple: [Mr. Mom, 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. Jorja Curtright
    Jorja Curtright was an American actress and novelist best known for her work in mid-20th-century film and television and for being married to writer Sidney Sheldon.
  • E. 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."
  • 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_69d822e1a2cc81908e5bb93cf61ce3cc completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb51a562c819098971447db4b29f7 completed April 14, 2026, 9:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69fdd5de0b98819094c32765e4cb3f9c completed May 8, 2026, 12:23 p.m.
Created at: April 10, 2026, 1:27 a.m.