Triple

T23349879
Position Surface form Disambiguated ID Type / Status
Subject Anne Carey E591975 entity
Predicate notableWork P4 FINISHED
Object Adventureland 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: Adventureland | Statement: [Anne Carey, notableWork, Adventureland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Adventureland
Context triple: [Anne Carey, 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. Adventureland amusement park
    Adventureland amusement park is a popular Midwestern theme park featuring a wide variety of rides, roller coasters, and family attractions.
  • E. Discoveryland
    Discoveryland is a retro-futuristic themed land at Disneyland Paris inspired by the visionary works of Jules Verne and classic science fiction.
  • 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_69e25d20e3d08190bcede87673cafb25 completed April 17, 2026, 4:17 p.m.
NER Named-entity recognition batch_69f19a132da08190b30de610d34c96cc completed April 29, 2026, 5:41 a.m.
Created at: April 17, 2026, 5:19 p.m.