Triple

T9473375
Position Surface form Disambiguated ID Type / Status
Subject Stephen Chbosky E228449 entity
Predicate notableWork P4 FINISHED
Object Wonder (film) E554427 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: Wonder (film) | Statement: [Stephen Chbosky, notableWork, Wonder (film)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wonder (film)
Context triple: [Stephen Chbosky, notableWork, Wonder (film)]
  • A. Wonder chosen
    Wonder is a 2017 drama film about a boy with a facial difference navigating school and family life, based on R.J. Palacio’s novel of the same name.
  • B. To the Wonder
    To the Wonder is a 2012 romantic drama film directed by Terrence Malick, noted for its poetic, visually driven storytelling and Emmanuel Lubezki’s lyrical cinematography.
  • C. The Wonder
    The Wonder is a psychological period drama film in which Florence Pugh plays an English nurse sent to investigate a young Irish girl who appears to survive without eating.
  • D. Isles of Wonder
    Isles of Wonder was the theatrical, cinematic-themed opening ceremony of the London 2012 Olympic Games, directed by Danny Boyle and celebrating British history and culture.
  • E. Wish
    Wish is an e-commerce platform and mobile shopping app known for offering a wide variety of low-priced goods shipped directly from merchants, primarily in China, to consumers worldwide.
  • 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_69ca847162c48190b079076c9595513c completed March 30, 2026, 2:10 p.m.
NER Named-entity recognition batch_69cd7ff0afd08190871b68a88fdbff2b completed April 1, 2026, 8:28 p.m.
NED1 Entity disambiguation (via context triple) batch_69d122d625fc8190b8222930449ad3da completed April 4, 2026, 2:40 p.m.
Created at: March 30, 2026, 7:54 p.m.