Triple

T19547235
Position Surface form Disambiguated ID Type / Status
Subject So Big E489104 entity
Predicate hasAdaptation P1690 FINISHED
Object So Big (1953 film) 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: So Big (1953 film) | Statement: [So Big, hasAdaptation, So Big (1953 film)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: So Big (1953 film)
Context triple: [So Big, hasAdaptation, So Big (1953 film)]
  • A. So Big (1953 film) chosen
    So Big (1953 film) is a 1953 American drama film adaptation of Edna Ferber’s Pulitzer Prize-winning novel, starring Sterling Hayden.
  • B. So Big (1932 film)
    So Big (1932 film) is a 1932 drama movie adaptation of Edna Ferber’s Pulitzer Prize–winning novel, focusing on a woman’s struggles and resilience in rural America.
  • C. So Big (1924 film)
    So Big (1924 film) is a silent drama movie adaptation of Edna Ferber’s Pulitzer Prize–winning novel, depicting a young widow’s struggles and perseverance on a Midwestern farm.
  • D. Love in the Big City
    Love in the Big City is a popular Russian-Ukrainian romantic comedy film known for its lighthearted take on modern relationships and urban life.
  • E. The Bad and the Beautiful
    The Bad and the Beautiful is a 1952 American film noir–style drama about the ruthless rise of a Hollywood producer, acclaimed for its incisive look at the movie industry and its multiple Academy Award-winning cinematography.
  • 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_69d8e8db5b6c8190984b61f91981f575 completed April 10, 2026, 12:11 p.m.
NER Named-entity recognition batch_69e63d2d8a6081908e9bb3a6f5d85896 completed April 20, 2026, 2:50 p.m.
Created at: April 10, 2026, 1:41 p.m.