Triple

T15991763
Position Surface form Disambiguated ID Type / Status
Subject Rysher Entertainment E387846 entity
Predicate notableWork P4 FINISHED
Object Kingpin E1120264 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: Kingpin | Statement: [Rysher Entertainment, notableWork, Kingpin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kingpin
Context triple: [Rysher Entertainment, notableWork, Kingpin]
  • A. Kingpin
    Kingpin is a powerful crime lord in the Marvel Universe, best known as a major adversary of heroes like Spider-Man and Daredevil.
  • B. Kingpin chosen
    Kingpin is a 2003 television film that dramatizes the rise and fall of a powerful Mexican drug lord and his cartel.
  • C. Kingpin
    Kingpin is a 1996 sports comedy film about a washed-up former bowling prodigy who mentors an Amish bowling talent, known for its offbeat humor and cult following.
  • D. Kingpin Suite
    Kingpin Suite is a luxury, bowling-themed hotel suite in Las Vegas known for its in-room bowling lanes and over-the-top entertainment amenities.
  • E. The Drug King
    The Drug King is a South Korean crime drama film that chronicles the rise and fall of a small-time smuggler who becomes a powerful drug lord in 1970s Busan.
  • 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_69d86daa562c81908aacc179c0fe8fb5 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e157844ed881908b42bfc1bb740d4e completed April 16, 2026, 9:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffc3d3ef2881909213ff608192f1ef completed May 9, 2026, 11:31 p.m.
Created at: April 10, 2026, 4:54 a.m.