Triple

T20397620
Position Surface form Disambiguated ID Type / Status
Subject Inspector William "Bill" Henderson E500249 entity
Predicate worksIn P1527 FINISHED
Object Metropolis NE NERFINISHED

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: Metropolis | Statement: [Inspector William "Bill" Henderson, worksIn, Metropolis]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Metropolis
Context triple: [Inspector William "Bill" Henderson, worksIn, Metropolis]
  • A. Metropolis
    Metropolis is a landmark 1927 German expressionist science-fiction film directed by Fritz Lang, renowned for its pioneering special effects and dystopian vision of a futuristic urban society.
  • B. Metropolis
    Metropolis is a major Ethereum protocol upgrade that introduced significant improvements to scalability, security, and usability of the blockchain.
  • C. Metropolis
    Metropolis is a science fiction television series inspired by Fritz Lang’s classic 1927 film, exploring a futuristic city divided by class and technological power.
  • D. Metropolis
    Metropolis is a historical crime novel by Philip Kerr featuring detective Bernie Gunther in a pre-World War II Berlin setting.
  • E. Metropolis chosen
    Metropolis is a DC Comics–themed area in various Six Flags parks, styled as Superman’s iconic city with related rides and attractions.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b4a81bec8190b69adfdc1336a015 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6798c2b28819092fab93f01218cde completed April 20, 2026, 7:07 p.m.
Created at: April 16, 2026, 11:29 a.m.