Triple

T16008734
Position Surface form Disambiguated ID Type / Status
Subject Dana Goldberg E388283 entity
Predicate notableWork P4 FINISHED
Object 6 Underground E86001 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: 6 Underground | Statement: [Dana Goldberg, notableWork, 6 Underground]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: 6 Underground
Context triple: [Dana Goldberg, notableWork, 6 Underground]
  • A. 6 Underground chosen
    6 Underground is a 2019 high-octane action film directed by Michael Bay, starring Ryan Reynolds as the leader of a covert vigilante squad that fakes their deaths to take down notorious criminals.
  • B. Underground
    Underground is a non-fiction book by Haruki Murakami that explores the 1995 Tokyo subway sarin gas attack through interviews with victims and members of the Aum Shinrikyo cult.
  • C. Underground
    Underground is a dystopian political thriller novel by Australian author Andrew McGahan that explores authoritarianism, surveillance, and resistance in a near-future Australia.
  • D. Underground
    Underground is a 1941 World War II espionage film in which Karen Verne plays a key role, contributing to her recognition as a German-born actress in Hollywood.
  • E. Underground
    Underground is the rapid transit system serving London and some surrounding areas, commonly known as the London Underground or the Tube.
  • 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_69d86dabcb7c8190b6a39d6831d2fa1b completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e15801a58881909805d0b90011e6ff completed April 16, 2026, 9:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffcf22db3481909141ddef151d0341 completed May 10, 2026, 12:19 a.m.
Created at: April 10, 2026, 4:55 a.m.