Triple

T18912207
Position Surface form Disambiguated ID Type / Status
Subject Pretty in Pink E462635 entity
Predicate editedBy P1954 FINISHED
Object Richard Marks NE NERFINISHED

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: Richard Marks | Statement: [Pretty in Pink, editedBy, Richard Marks]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Richard Marks
Context triple: [Pretty in Pink, editedBy, Richard Marks]
  • A. Richard Marks chosen
    Richard Marks was an American film editor known for his work on numerous acclaimed movies, including the culinary drama "Julie & Julia."
  • B. John Marks
    John Marks is an American author and journalist known for his investigative and political writing, including collaborations with fellow reporter Joseph Medill Patterson Albright.
  • C. Matthew Marks
    Matthew Marks is an influential American art dealer and gallerist known for representing prominent contemporary artists through his eponymous gallery.
  • D. Bill Marks
    Bill Marks is the troubled yet determined U.S. federal air marshal portrayed by Liam Neeson in the action-thriller film "Non-Stop."
  • E. Dennis Howard Marks
    Dennis Howard Marks was a Welsh drug smuggler turned author and counterculture icon, best known for his large-scale cannabis trafficking in the 1970s and 1980s and his later memoir "Mr Nice."
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8dcfd05bc819088903cca13cc2846 completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5c6238e288190b30311b5d80beafb completed April 20, 2026, 6:22 a.m.
Created at: April 10, 2026, 11:58 a.m.