Triple

T11894314
Position Surface form Disambiguated ID Type / Status
Subject Gardaland E282996 entity
Predicate hasResort P4287 FINISHED
Object Gardaland Hotel E282996 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: Gardaland Hotel | Statement: [Gardaland, hasResort, Gardaland Hotel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gardaland Hotel
Context triple: [Gardaland, hasResort, Gardaland Hotel]
  • A. Gardaland chosen
    Gardaland is a major Italian theme park and resort near Lake Garda, known for its roller coasters, family attractions, and themed entertainment.
  • B. Mirabilandia
    Mirabilandia is a major Italian amusement park near Ravenna, known for its large roller coasters, themed areas, and water park on the Adriatic coast.
  • C. Vasa Park Resort
    Vasa Park Resort is a lakeside recreational and event venue on the shores of Lake Sammamish in Washington State, offering swimming, picnicking, and facilities for gatherings.
  • D. Lucas Gardens
    Lucas Gardens is a small public park in the Denmark Hill area of London, offering green space, trees, and seating for local residents.
  • E. Tivoli Park
    Tivoli Park is the largest and most famous public park in Ljubljana, Slovenia, known for its landscaped gardens, walking paths, and cultural venues.
  • 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_69d6ab2a90b08190a4e818821cc93e6d completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d8dd1172988190a2c13d37220f2f93 completed April 10, 2026, 11:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69f4180569ac81909137d56374e800c0 completed May 1, 2026, 3:03 a.m.
Created at: April 8, 2026, 9:44 p.m.