Triple

T6166939
Position Surface form Disambiguated ID Type / Status
Subject Enemy E137588 entity
Predicate portrayedBy P1507 FINISHED
Object Jake Gyllenhaal E303090 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: Jake Gyllenhaal | Statement: [Enemy, portrayedBy, Jake Gyllenhaal]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jake Gyllenhaal
Context triple: [Enemy, portrayedBy, Jake Gyllenhaal]
  • A. Jake Gyllenhaal chosen
    Jake Gyllenhaal is an acclaimed American actor known for his intense, versatile performances in films ranging from independent dramas to major studio thrillers.
  • B. Stephen Gyllenhaal
    Stephen Gyllenhaal is an American film and television director and poet, known for directing dramas such as "Waterland" and for being the father of actors Jake and Maggie Gyllenhaal.
  • C. Emile Hirsch
    Emile Hirsch is an American actor known for his intense performances in films such as "Into the Wild," "Milk," and "Lone Survivor."
  • D. Logan Lerman
    Logan Lerman is an American actor best known for his lead role in the "Percy Jackson" film series and performances in movies such as "The Perks of Being a Wallflower" and "Fury."
  • E. Dane DeHaan
    Dane DeHaan is an American actor known for his intense, often offbeat performances in films such as Chronicle, The Amazing Spider-Man 2, and Valerian and the City of a Thousand Planets.
  • 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_69c008a68c508190a8d78245c865960e completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c05d6366948190b35ef44dd9d6fcd6 completed March 22, 2026, 9:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69c243cbd38081909169c57c55696ff6 completed March 24, 2026, 7:57 a.m.
Created at: March 22, 2026, 4:17 p.m.