Triple

T8008608
Position Surface form Disambiguated ID Type / Status
Subject Jumanji: The Next Level E186426 entity
Predicate screenwriter P2831 FINISHED
Object Scott Rosenberg E300554 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: Scott Rosenberg | Statement: [Jumanji: The Next Level, screenwriter, Scott Rosenberg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Scott Rosenberg
Context triple: [Jumanji: The Next Level, screenwriter, Scott Rosenberg]
  • A. Scott Rosenberg chosen
    Scott Rosenberg is an American screenwriter and producer known for writing high-profile films such as "Con Air," "Gone in 60 Seconds," and "High Fidelity."
  • B. Mark Rosenberg
    Mark Rosenberg was an American film producer known for his work on notable movies of the 1980s and early 1990s.
  • C. Dave Rosenberg
    Dave Rosenberg is a technology entrepreneur best known as a co-founder of MuleSoft, a leading integration and API management platform company.
  • D. Josh Kesselman
    Josh Kesselman is a film and television producer best known for his work as an executive producer on projects such as the series "The Great."
  • E. Jeff Kodosky
    Jeff Kodosky is an American engineer and co-founder of National Instruments, best known as the "father of LabVIEW" for creating the influential graphical programming environment.
  • 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_69ca82abaffc8190ab8af79cdbc31ab3 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3d6e1c9081909018bcebb18906f6 completed March 31, 2026, 3:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69cde6dc7b248190b59187a80b4fe036 completed April 2, 2026, 3:47 a.m.
Created at: March 30, 2026, 5:19 p.m.