Triple

T8129237
Position Surface form Disambiguated ID Type / Status
Subject 13 Going on 30 E189813 entity
Predicate editedBy P1954 FINISHED
Object Susan Littenberg E613698 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: Susan Littenberg | Statement: [13 Going on 30, editedBy, Susan Littenberg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Susan Littenberg
Context triple: [13 Going on 30, editedBy, Susan Littenberg]
  • A. Susan Littenberg chosen
    Susan Littenberg is a film editor known for her work on feature films such as the teen comedy "Easy A."
  • B. Judy Levitt
    Judy Levitt is an American actress best known for her long marriage to Star Trek actor Walter Koenig and for appearing in several of his film and television projects.
  • C. Arlene Litman
    Arlene Litman was the mother of American actress Lisa Bonet.
  • D. Liz Friedlander
    Liz Friedlander is an American director best known for her work on music videos for major pop and rock artists as well as episodes of popular television series.
  • E. Janet Margolin
    Janet Margolin was an American film and television actress best known for her roles in movies such as "David and Lisa" and Woody Allen's "Annie Hall."
  • 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_69ca82bcb4848190a9a9d036ad768642 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb43b6b4dc8190be237e6dd21c863b completed March 31, 2026, 3:47 a.m.
NED1 Entity disambiguation (via context triple) batch_69ce0218a3888190a0b224cf93a511de completed April 2, 2026, 5:43 a.m.
Created at: March 30, 2026, 5:34 p.m.