Triple

T15586497
Position Surface form Disambiguated ID Type / Status
Subject Bayerisches Nationalmuseum E374634 entity
Predicate locatedNear P294 FINISHED
Object Englischer Garten E117562 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: Englischer Garten | Statement: [Bayerisches Nationalmuseum, locatedNear, Englischer Garten]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Englischer Garten
Context triple: [Bayerisches Nationalmuseum, locatedNear, Englischer Garten]
  • A. Englischer Garten chosen
    Englischer Garten is a large public park in Munich, Germany, renowned for its expansive green spaces, beer gardens, and riverside surfing on the Eisbach.
  • B. Karlsaue Park
    Karlsaue Park is a historic baroque landscape park in Kassel, Germany, known for its formal gardens, expansive meadows, and integration of art and architecture.
  • C. Hofgarten
    The Hofgarten is a historic Renaissance-style court garden in central Munich, known for its arcades, pavilions, and role as a popular public park and cultural venue.
  • D. Schillerpark
    Schillerpark is a historic public park in Berlin known for its expansive lawns, tree-lined paths, and role as a popular recreational area for local residents.
  • E. Tiergarten
    Tiergarten is a large central park in Berlin known for its expansive green spaces, monuments, and cultural landmarks.
  • 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_69d85ccd575081908909b71a3f3e3a61 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04e4900408190aadb48b001db4169 completed April 16, 2026, 2:49 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff4c5166d88190ab14c7779e3e8e1f completed May 9, 2026, 3:01 p.m.
Created at: April 10, 2026, 4:11 a.m.