Triple

T21437247
Position Surface form Disambiguated ID Type / Status
Subject I Love You, Man E528844 entity
Predicate cinematography P1953 FINISHED
Object Lawrence Sher 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: Lawrence Sher | Statement: [I Love You, Man, cinematography, Lawrence Sher]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lawrence Sher
Context triple: [I Love You, Man, cinematography, Lawrence Sher]
  • A. Lawrence Sher chosen
    Lawrence Sher is an American cinematographer best known for his visually distinctive work on films such as Joker (2019) and The Hangover series.
  • B. Lawrence Lacks
    Lawrence Lacks is the eldest son of Henrietta Lacks, whose immortal HeLa cells became foundational to modern biomedical research.
  • C. Lawrence Wolf
    Lawrence Wolf is an actor best known for his role in the underground cult film "Chafed Elbows" (1966), a satirical work by director Robert Downey Sr.
  • D. Lawrence Sanders
    Lawrence Sanders was an American crime novelist best known for his bestselling thrillers and detective series, including the Edward X. Delaney and Archy McNally books.
  • E. Lawrence Bender
    Lawrence Bender is an American film producer best known for his longtime collaboration with Quentin Tarantino on influential films such as "Pulp Fiction" and the "Kill Bill" series.
  • 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_69e0c4569fa081908101baa24f8745db completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69ee8140a1fc8190bedf297cfc4d4841 completed April 26, 2026, 9:18 p.m.
Created at: April 16, 2026, 6:03 p.m.