Triple

T11338008
Position Surface form Disambiguated ID Type / Status
Subject Michael Zegen E268520 entity
Predicate notableWork P4 FINISHED
Object Adventureland E349318 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: Adventureland | Statement: [Michael Zegen, notableWork, Adventureland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Adventureland
Context triple: [Michael Zegen, notableWork, Adventureland]
  • A. Adventureland
    Adventureland is a themed land found in several Disney parks, designed to evoke exotic, tropical locales through attractions, lush landscaping, and immersive storytelling.
  • B. Adventureland chosen
    Adventureland is a 2009 coming-of-age comedy-drama film set in a 1980s amusement park, known for its blend of humor and bittersweet romance.
  • C. Adventure Land
    Adventure Land is a themed area within Europa-Park that immerses visitors in adventurous, exploration-inspired settings and attractions.
  • D. Discoveryland
    Discoveryland is a retro-futuristic themed land at Disneyland Paris inspired by the visionary works of Jules Verne and classic science fiction.
  • E. Adventureland (Hong Kong Disneyland)
    Adventureland at Hong Kong Disneyland is a themed area inspired by exotic jungles and tropical exploration, featuring attractions, entertainment, and environments that evoke adventure in remote wilderness settings.
  • 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_69d6aacb1f0881908c84a349fd1be047 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7ea008b5081908e6c6c6fc29ef936 completed April 9, 2026, 6:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69e5432abfd081909d1bbf6460643fb9 completed April 19, 2026, 9:03 p.m.
Created at: April 8, 2026, 9:33 p.m.