Triple

T8782289
Position Surface form Disambiguated ID Type / Status
Subject Kathryn Hahn E208758 entity
Predicate notableWork P4 FINISHED
Object Tomorrowland E508785 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: Tomorrowland | Statement: [Kathryn Hahn, notableWork, Tomorrowland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tomorrowland
Context triple: [Kathryn Hahn, notableWork, Tomorrowland]
  • A. 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.
  • B. Tomorrowland
    Tomorrowland is one of the world’s largest and most famous electronic dance music festivals, held annually in Boom, Belgium.
  • C. Tomorrowland (film) chosen
    Tomorrowland is a 2015 science-fiction adventure film directed by Brad Bird that follows a gifted teenager and a disillusioned inventor who discover a futuristic parallel world and attempt to save both it and Earth from impending catastrophe.
  • D. Tomorrowland station
    Tomorrowland station is a themed railroad stop within Disneyland’s Tomorrowland area, serving as one of the stations on the Disneyland Railroad.
  • E. The Great Movie Ride
    The Great Movie Ride was a now-closed dark ride at Disney's Hollywood Studios that took guests on a journey through scenes from classic films using elaborate sets, animatronics, and live actors.
  • 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_69ca835fbee88190bf625939bac48d7f completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc5f7155b081908891e84b704f0ebf completed March 31, 2026, 11:57 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf51e9d97c8190a947848fdaa5b67d completed April 3, 2026, 5:36 a.m.
Created at: March 30, 2026, 6:42 p.m.