Triple

T14257412
Position Surface form Disambiguated ID Type / Status
Subject Mitte E353420 entity
Predicate contains P35 FINISHED
Object Tiergarten park 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 park | Statement: [Mitte, contains, Tiergarten park]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tiergarten park
Context triple: [Mitte, contains, Tiergarten park]
  • A. Tiergarten park
    Tiergarten park is a historic public park in the German town of Kleve, known for its landscaped grounds, walking paths, and recreational green spaces.
  • B. Tiergarten chosen
    Tiergarten is a large central park in Berlin known for its expansive green spaces, monuments, and cultural landmarks.
  • 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. Volkspark Friedrichshain
    Volkspark Friedrichshain is a large historic public park in Berlin known for its wooded hills, walking paths, and recreational facilities.
  • E. Schlosspark Charlottenburg
    Schlosspark Charlottenburg is a historic baroque and landscaped palace garden in Berlin, surrounding Charlottenburg Palace and featuring ornamental lakes, sculptures, and tree-lined promenades.
  • 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_69d8278c43e08190824146f4632b89a5 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de6352611c819090d062fe3079cd03 completed April 14, 2026, 3:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd325f213881909acf776ff4831c30 completed May 8, 2026, 12:46 a.m.
Created at: April 10, 2026, 1:09 a.m.