Triple

T13032187
Position Surface form Disambiguated ID Type / Status
Subject Terry Malloy E326466 entity
Predicate enemyOf P437 FINISHED
Object Johnny Friendly E943806 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: Johnny Friendly | Statement: [Terry Malloy, enemyOf, Johnny Friendly]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Johnny Friendly
Context triple: [Terry Malloy, enemyOf, Johnny Friendly]
  • A. Johnny Friendly chosen
    Johnny Friendly is the corrupt and ruthless mob-connected union boss who serves as the main antagonist in the classic film "On the Waterfront."
  • B. Officer Bill Lockwood
    Officer Bill Lockwood is a fictional police officer featured as a central character in the classic American radio crime drama series "Dragnet."
  • C. Stan Fink
    Stan Fink is a supporting character in the film "Eternal Sunshine of the Spotless Mind," working as a technician for the memory-erasing company Lacuna, Inc.
  • D. Lester Cole
    Lester Cole was an American screenwriter and one of the Hollywood Ten, blacklisted during the Red Scare for alleged communist affiliations.
  • E. Walt Kowalski
    Walt Kowalski is a gruff, widowed Korean War veteran whose evolving relationship with his Hmong neighbors drives the emotional and moral core of the film "Gran Torino."
  • 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_69d8076cc45c81908123123f43e69266 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d97efe72348190b52fb4068f5fb829 completed April 10, 2026, 10:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6e269c18481908e0b46c298a946ca completed May 3, 2026, 5:51 a.m.
Created at: April 9, 2026, 8:54 p.m.