Triple

T8881454
Position Surface form Disambiguated ID Type / Status
Subject Eric Szmanda E211419 entity
Predicate appearedIn P795 FINISHED
Object Snow Wonder
Snow Wonder is a 2005 made-for-television holiday drama film that intertwines multiple characters' lives during a Christmas Eve snowstorm.
E764181 NE FINISHED

How this triple was built (4 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: Snow Wonder | Statement: [Eric Szmanda, appearedIn, Snow Wonder]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Snow Wonder
Context triple: [Eric Szmanda, appearedIn, Snow Wonder]
  • A. Snowfall
    Snowfall is an American crime drama television series that explores the early days of the crack cocaine epidemic in 1980s Los Angeles.
  • B. Snowman
    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.
  • C. 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.
  • D. Snowbird
    Snowbird is a major ski and snowboard resort in Utah known for its steep terrain, deep powder, and long winter season.
  • E. The Slopes
    The Slopes is a historic landscaped park in Buxton, Derbyshire, known for its terraced walks, ornamental gardens, and views over the town’s spa architecture.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Snow Wonder
Triple: [Eric Szmanda, appearedIn, Snow Wonder]
Generated description
Snow Wonder is a 2005 made-for-television holiday drama film that intertwines multiple characters' lives during a Christmas Eve snowstorm.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Snow Wonder
Target entity description: Snow Wonder is a 2005 made-for-television holiday drama film that intertwines multiple characters' lives during a Christmas Eve snowstorm.
  • A. Snowfall
    Snowfall is an American crime drama television series that explores the early days of the crack cocaine epidemic in 1980s Los Angeles.
  • B. Snowman
    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.
  • C. 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.
  • D. Snowbird
    Snowbird is a major ski and snowboard resort in Utah known for its steep terrain, deep powder, and long winter season.
  • E. The Slopes
    The Slopes is a historic landscaped park in Buxton, Derbyshire, known for its terraced walks, ornamental gardens, and views over the town’s spa architecture.
  • F. None of above. chosen

Provenance (5 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_69ca838f9e20819096ab1f236a70381a completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc6168e3d881908c58cf11cf5f9a0e completed April 1, 2026, 12:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfabca74888190934593d6504fbed1 completed April 3, 2026, noon
NEDg Description generation batch_69cfac9c743c8190a26f753111f07281 completed April 3, 2026, 12:03 p.m.
NED2 Entity disambiguation (via description) batch_69cfad54cf5c81908558fd4c21f2f3b6 completed April 3, 2026, 12:06 p.m.
Created at: March 30, 2026, 6:53 p.m.