Triple

T4545460
Position Surface form Disambiguated ID Type / Status
Subject Oryx E110035 entity
Predicate associatedWith P37 FINISHED
Object Snowman E110034 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: Snowman | Statement: [Oryx, associatedWith, Snowman]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Snowman
Context triple: [Oryx, associatedWith, Snowman]
  • A. Snowman chosen
    Snowman is the post-apocalyptic survivor and narrator of Margaret Atwood’s dystopian novel "Oryx and Crake," through whose perspective the story’s ruined world and its origins are revealed.
  • B. The Snowman
    The Snowman is a 2017 crime thriller film adaptation of Jo Nesbø’s novel, following a detective hunting a serial killer who leaves snowmen at his crime scenes.
  • C. Snowfall
    Snowfall is an American crime drama television series that explores the early days of the crack cocaine epidemic in 1980s Los Angeles.
  • D. Thunder Snow
    Thunder Snow is a prominent Irish-bred Thoroughbred racehorse best known for winning back-to-back Dubai World Cups in 2018 and 2019.
  • E. Mister Snow
    "Mister Snow" is a romantic character song from Rodgers and Hammerstein's classic musical "Carousel," sung by the heroine as she imagines a future with her suitor, Enoch Snow.
  • 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_69bd4412524c8190be5bcc9ddee91848 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd57d62a8481909d5d803a582c76a1 completed March 20, 2026, 2:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69bdb936f3348190af0784d472bff312 completed March 20, 2026, 9:16 p.m.
Created at: March 20, 2026, 1:05 p.m.