Triple

T6230923
Position Surface form Disambiguated ID Type / Status
Subject S7 E139349 entity
Predicate viaStation P64719 FINISHED
Object Tiergarten E106564 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: Tiergarten | Statement: [S7, viaStation, Tiergarten]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tiergarten
Context triple: [S7, viaStation, Tiergarten]
  • A. Tiergarten chosen
    Tiergarten is a large central park in Berlin known for its expansive green spaces, monuments, and cultural landmarks.
  • B. 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.
  • C. Volksgarten
    Volksgarten is a historic public park in central Vienna renowned for its formal rose gardens, neoclassical monuments, and location along the Ringstrasse.
  • 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. Georgengarten
    Georgengarten is a large English-style landscape park in Hanover, Germany, known for its expansive lawns, tree-lined avenues, and integration into the historic Herrenhausen Gardens ensemble.
  • 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_69c008afd3148190b71e9eaa60420dd1 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c062ec5be4819084d6df2e8dd2a542 completed March 22, 2026, 9:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69c20de90ba08190be1e0c44b5b13f53 completed March 24, 2026, 4:07 a.m.
Created at: March 22, 2026, 4:22 p.m.