Triple

T2538687
Position Surface form Disambiguated ID Type / Status
Subject Giant Ferris Wheel E56330 entity
Predicate location P40 FINISHED
Object Prater E56329 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: Prater | Statement: [Giant Ferris Wheel, location, Prater]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Prater
Context triple: [Giant Ferris Wheel, location, Prater]
  • A. Prater chosen
    Prater is a large public park and historic amusement area in Vienna, Austria, best known for its iconic Giant Ferris Wheel and extensive green spaces.
  • B. Gellert Park
    Gellert Park is a public recreational park located in Daly City, California, offering open green spaces and community amenities for local residents.
  • C. Fürth
    Fürth is a historic city in northern Bavaria, Germany, known for its well-preserved old town and proximity to Nuremberg within the Franconian metropolitan region.
  • D. Prater Tower
    Prater Tower is a prominent amusement ride and observation tower located in Vienna’s historic Prater park.
  • E. Untermarkt
    Untermarkt is the historic lower market square in Görlitz, Germany, known for its well-preserved medieval and Renaissance architecture.
  • 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_69ab4a49b6508190bc467fbef4bac334 completed March 6, 2026, 9:42 p.m.
NER Named-entity recognition batch_69abd298fc2481908b2925bf6a06532b completed March 7, 2026, 7:24 a.m.
NED1 Entity disambiguation (via context triple) batch_69af65573c30819080a8f0235e5dd8be completed March 10, 2026, 12:27 a.m.
Created at: March 6, 2026, 9:47 p.m.