Triple

T21736230
Position Surface form Disambiguated ID Type / Status
Subject Medicine Man E536530 entity
Predicate productionCompany P490 FINISHED
Object Cinergi Pictures 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: Cinergi Pictures | Statement: [Medicine Man, productionCompany, Cinergi Pictures]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cinergi Pictures
Context triple: [Medicine Man, productionCompany, Cinergi Pictures]
  • A. Cinergi Pictures chosen
    Cinergi Pictures was an independent American film production company active in the 1990s, known for producing big-budget Hollywood films across various genres.
  • B. Tri-Star Pictures
    Tri-Star Pictures is an American film production and distribution company known for releasing a wide range of Hollywood movies since the 1980s.
  • C. Equity Pictures
    Equity Pictures is a film production company known for financing and producing a range of international motion pictures.
  • D. Myriad Pictures
    Myriad Pictures is an independent film production and distribution company known for handling a range of arthouse and specialty films.
  • E. Triton Pictures
    Triton Pictures was an independent American film distribution company known for releasing arthouse and foreign films in the early 1990s.
  • 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_69e0c46df5448190b4322127ffc4c690 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69effd0c0a088190bd1926fa4b73d8f4 completed April 28, 2026, 12:19 a.m.
Created at: April 16, 2026, 6:49 p.m.