Triple

T19562831
Position Surface form Disambiguated ID Type / Status
Subject Mann Theatres E489498 entity
Predicate foundedBy P104 FINISHED
Object Ted Mann 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: Ted Mann | Statement: [Mann Theatres, foundedBy, Ted Mann]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ted Mann
Context triple: [Mann Theatres, foundedBy, Ted Mann]
  • A. Ted Mann chosen
    Ted Mann was an American film exhibitor and businessman best known for owning the Mann Theatres chain and the historic Grauman’s Chinese Theatre in Hollywood.
  • B. Larry Keele
    Larry Keele is an American investor and co-founder of Oaktree Capital Management, a leading global alternative investment firm specializing in credit and distressed debt.
  • C. Tom Mann
    Tom Mann was a prominent British trade unionist and socialist leader known for his key role in the New Unionism movement and early labor organizing in the late 19th and early 20th centuries.
  • D. Eric Muhlmann
    Eric Muhlmann was an individual significant enough in his field or community to have the Maria and Eric Muhlmann Award named in his honor.
  • E. Chris Kramer
    Chris Kramer is an American professional basketball player known for his impactful play as a guard in European leagues, particularly in Germany.
  • 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_69d8e8dc5d8c8190a6d7bd8864f43ca0 completed April 10, 2026, 12:11 p.m.
NER Named-entity recognition batch_69e63f74fdb08190852461b5d5c954ac completed April 20, 2026, 3 p.m.
Created at: April 10, 2026, 1:42 p.m.