Triple

T2151960
Position Surface form Disambiguated ID Type / Status
Subject Onward E47799 entity
Predicate writer P1360 FINISHED
Object Dan Scanlon E90893 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: Dan Scanlon | Statement: [Onward, writer, Dan Scanlon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dan Scanlon
Context triple: [Onward, writer, Dan Scanlon]
  • A. Dan Scanlon chosen
    Dan Scanlon is an American filmmaker and animator best known for his work as a director and writer at Pixar Animation Studios.
  • B. David Frankel
    David Frankel is an American film and television director best known for helming popular works such as "The Devil Wears Prada" and episodes of "Sex and the City."
  • C. James DeMonaco
    James DeMonaco is an American filmmaker and screenwriter best known for creating and writing the dystopian horror franchise "The Purge."
  • D. Dan Goor
    Dan Goor is an American television writer and producer best known for co-creating the comedy series "Brooklyn Nine-Nine" and his work on shows like "Parks and Recreation."
  • E. Brian Koppelman
    Brian Koppelman is an American screenwriter, director, and producer best known for co-writing films like "Rounders" and "Ocean's Thirteen" and co-creating the TV series "Billions."
  • 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_69a88a1d1fd8819088b34990d69a712f completed March 4, 2026, 7:38 p.m.
NER Named-entity recognition batch_69abbe48ad148190a7d6cc88fd38a660 completed March 7, 2026, 5:57 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae5d940bec8190998ef88ed44e5811 completed March 9, 2026, 5:41 a.m.
Created at: March 4, 2026, 7:44 p.m.