Triple

T20188035
Position Surface form Disambiguated ID Type / Status
Subject Goliath (Walibi Holland) E492911 entity
Predicate owner P347 FINISHED
Object Walibi Holland 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: Walibi Holland | Statement: [Goliath (Walibi Holland), owner, Walibi Holland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Walibi Holland
Context triple: [Goliath (Walibi Holland), owner, Walibi Holland]
  • A. Walibi Holland chosen
    Walibi Holland is a major amusement park in the Netherlands known for its intense roller coasters and thrill rides.
  • B. Walibi Belgium
    Walibi Belgium is a major Belgian theme park known for its roller coasters and family attractions, located in Wavre near Brussels.
  • C. Avonturenpark Hellendoorn
    Avonturenpark Hellendoorn is a family-oriented amusement park in the Dutch town of Hellendoorn, featuring a variety of rides, shows, and themed attractions.
  • D. Efteling
    Efteling is a major Dutch fantasy-themed amusement park and resort known for its fairy-tale attractions and immersive storytelling.
  • E. Kennispark Twente
    Kennispark Twente is an innovation and business park in Enschede, the Netherlands, focused on fostering high-tech startups and knowledge-intensive companies in close collaboration with the University of Twente.
  • 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_69da6268a034819081cbd9ea5a1c9475 completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e66ad2c43c8190a2fc5ef2a0514e53 completed April 20, 2026, 6:05 p.m.
Created at: April 11, 2026, 11:37 p.m.