Triple

T16858739
Position Surface form Disambiguated ID Type / Status
Subject Berlin-Moabit E409853 entity
Predicate contains P35 FINISHED
Object Kleiner Tiergarten E610259 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: Kleiner Tiergarten | Statement: [Berlin-Moabit, contains, Kleiner Tiergarten]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kleiner Tiergarten
Context triple: [Berlin-Moabit, contains, Kleiner Tiergarten]
  • A. Kleiner Tiergarten park chosen
    Kleiner Tiergarten park is a small urban green space in Berlin known for its tree-lined paths, lawns, and recreational areas within the Moabit district.
  • B. Tiergarten
    Tiergarten is a large central park in Berlin known for its expansive green spaces, monuments, and cultural landmarks.
  • 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. 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.
  • E. Unterer Schlossgarten
    Unterer Schlossgarten is a central urban park in Stuttgart, Germany, known for its green spaces, ponds, and recreational areas near the city center.
  • 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_69d88395e6c88190b22730f335107c14 completed April 10, 2026, 4:59 a.m.
NER Named-entity recognition batch_69e3b37ef4748190b149d98fc0ab4205 completed April 18, 2026, 4:38 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00c2a6f2c48190874839f78f943fdb completed May 10, 2026, 5:38 p.m.
Created at: April 10, 2026, 5:24 a.m.