Triple

T22415432
Position Surface form Disambiguated ID Type / Status
Subject Efteling E554110 entity
Predicate operator P179 FINISHED
Object Efteling B.V. 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: Efteling B.V. | Statement: [Efteling, operator, Efteling B.V.]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Efteling B.V.
Context triple: [Efteling, operator, Efteling B.V.]
  • A. Efteling chosen
    Efteling is a major Dutch fantasy-themed amusement park and resort known for its fairy-tale attractions and immersive storytelling.
  • B. Bobbejaanland
    Bobbejaanland is a Belgian theme park known for its family-friendly attractions, roller coasters, and live entertainment.
  • C. Dutch Wonderland
    Dutch Wonderland is a family-oriented amusement park in Lancaster, Pennsylvania, known for its kid-friendly rides, attractions, and castle-themed setting.
  • D. Groningen Europapark
    Groningen Europapark is a railway station in the city of Groningen, Netherlands, serving the Europapark district and providing regional train connections.
  • 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_69e11e4e6ce8819085a1e06d886bf21c completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f1594615f881909688b02548ee83eb completed April 29, 2026, 1:05 a.m.
Created at: April 16, 2026, 8:46 p.m.