Triple

T9341049
Position Surface form Disambiguated ID Type / Status
Subject DC Comics film adaptations E224762 entity
Predicate hasMainCharacter P1183 FINISHED
Object Green Lantern E101339 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: Green Lantern | Statement: [DC Comics film adaptations, hasMainCharacter, Green Lantern]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Green Lantern
Context triple: [DC Comics film adaptations, hasMainCharacter, Green Lantern]
  • A. Green Lantern chosen
    Green Lantern is a long-running DC Comics superhero franchise centered on intergalactic peacekeepers who wield power rings fueled by willpower.
  • B. Green Lantern
    Green Lantern is a stand-up steel roller coaster at Six Flags Great Adventure themed after the DC Comics superhero.
  • C. Mister Miracle
    Mister Miracle is a DC Comics superhero and master escape artist created by Jack Kirby as part of his Fourth World saga.
  • D. Guardian of the Universe
    Guardian of the Universe is an epithet for Gamera, the giant flying turtle kaiju from Japanese films who protects humanity from monstrous threats.
  • E. The Peacemaker
    The Peacemaker is a 1997 American action-thriller film about U.S. efforts to prevent nuclear terrorism, notable as the first film released by DreamWorks Pictures.
  • 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_69ca84286fcc81909f6e7fd7a7e862a2 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd4bafa9108190889397614756020d completed April 1, 2026, 4:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69d0e3f94ab88190a4c5a9129bd2ca14 completed April 4, 2026, 10:12 a.m.
Created at: March 30, 2026, 7:40 p.m.