Triple

T14226233
Position Surface form Disambiguated ID Type / Status
Subject Peter Pan (1954 musical) E352622 entity
Predicate hasSong P20452 FINISHED
Object Never Never Land E812342 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: Never Never Land | Statement: [Peter Pan (1954 musical), hasSong, Never Never Land]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Never Never Land
Context triple: [Peter Pan (1954 musical), hasSong, Never Never Land]
  • A. Never Land chosen
    Never Land is a magical, pirate-filled island from the Peter Pan universe where children never grow up and fantastical adventures take place.
  • B. Sky Land
    Sky Land is a high-altitude, cloud-filled world in Super Mario Bros. 3 known for its vertical level design and airborne challenges.
  • C. Tomorrowland
    Tomorrowland is a futuristic-themed land found in several Disney parks, featuring attractions and environments inspired by space travel, advanced technology, and visions of the future.
  • D. Tomorrowland
    Tomorrowland is one of the world’s largest and most famous electronic dance music festivals, held annually in Boom, Belgium.
  • E. Over the Moon
    "Over the Moon" is a popular Afropop track by Nigerian singer Dr SID, known for its catchy melody and dance-friendly production.
  • 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_69d8278a06e481908b5d6af0a8afe737 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de6228e53c8190abbe4e2d88a7362a completed April 14, 2026, 3:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd281801488190bcb17d27ee18cde6 completed May 8, 2026, 12:02 a.m.
Created at: April 10, 2026, 1:06 a.m.