Triple

T4506816
Position Surface form Disambiguated ID Type / Status
Subject Detective Comics E101348 entity
Predicate introducedCharacter P12208 FINISHED
Object Kate Kane E333023 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: Kate Kane | Statement: [Detective Comics, introducedCharacter, Kate Kane]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kate Kane
Context triple: [Detective Comics, introducedCharacter, Kate Kane]
  • A. Kate Kane chosen
    Kate Kane is the DC Comics superheroine who becomes Batwoman, a vigilante crime-fighter in Gotham City and cousin to Bruce Wayne.
  • B. Margaret Lemon
    Margaret Lemon is a fictional character on the television series "30 Rock," known as the overbearing and critical mother of protagonist Liz Lemon.
  • C. Diana Prince
    Diana Prince is the Amazonian warrior princess better known as Wonder Woman, a DC Comics superhero who leaves her hidden island to protect humanity.
  • D. Cassandra Cain
    Cassandra Cain is a DC Comics character and martial arts prodigy who becomes one of the heroes to take on the mantle of Batgirl.
  • E. Harley Quinn
    Harley Quinn is a chaotic, acrobatic antiheroine from DC Comics known for her clown-themed appearance, unpredictable behavior, and complex relationship with the Joker.
  • 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_69bd7f760658819085ca8c631894b486 completed March 20, 2026, 5:10 p.m.
Created at: March 20, 2026, 1:01 p.m.