Triple

T4506737
Position Surface form Disambiguated ID Type / Status
Subject DC Universe E101347 entity
Predicate hasFictionalCharacter P15645 FINISHED
Object Two-Face E126865 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: Two-Face | Statement: [DC Universe, hasFictionalCharacter, Two-Face]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Two-Face
Context triple: [DC Universe, hasFictionalCharacter, Two-Face]
  • A. Oswald Cobblepot
    Oswald Cobblepot, better known as the Penguin, is a deformed and vengeful crime lord in Gotham City who serves as one of Batman’s most iconic adversaries.
  • B. Bane
    Bane is a formidable masked villain in the Batman universe, known for his immense physical strength, strategic genius, and role as one of Batman’s most dangerous adversaries.
  • C. Harvey Dent chosen
    Harvey Dent is a prominent Gotham City district attorney who becomes the tragic, disfigured villain Two-Face in the Batman universe.
  • D. Joker
    Joker is a 2019 psychological thriller film centered on the origin story of Batman’s iconic nemesis, depicting his descent into madness and violence in a gritty, character-driven narrative.
  • E. Joker
    Joker is a DC Comics–themed roller coaster at Six Flags México known for its chaotic, unpredictable ride experience.
  • 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_69bd43d175248190894dc58b5b395c26 completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd570e7bb8819097f7a575384a10a8 completed March 20, 2026, 2:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69bd6f9d05d08190bde36e7d614a0e2e completed March 20, 2026, 4:02 p.m.
Created at: March 20, 2026, 1:01 p.m.