Triple

T19592434
Position Surface form Disambiguated ID Type / Status
Subject Tiergarten tunnel E470268 entity
Predicate traverses P416 FINISHED
Object Tiergarten district E969788 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 district | Statement: [Tiergarten tunnel, traverses, Tiergarten district]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tiergarten district
Context triple: [Tiergarten tunnel, traverses, Tiergarten district]
  • A. Tiergarten district chosen
    Tiergarten district is a central Berlin borough known for its expansive Tiergarten park, government buildings, and cultural landmarks.
  • B. Schöneberg district
    Schöneberg district is a central borough of Berlin, Germany, known for its vibrant cultural scene, historical significance, and diverse urban neighborhoods.
  • C. Charlottenburg
    Charlottenburg is a historic district in western Berlin, Germany, known for its baroque Charlottenburg Palace and role as a former independent city before incorporation into Berlin.
  • D. Nollendorfplatz area
    The Nollendorfplatz area is a lively Berlin neighborhood known for its historic square, vibrant LGBTQ+ scene, and mix of nightlife, cafés, and cultural venues.
  • E. Bornheim Mitte
    Bornheim Mitte is a central public transit station in Frankfurt’s Bornheim district, serving as a key stop on the city’s U-Bahn network.
  • 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_69d8e510024481908415c0d616fa6186 completed April 10, 2026, 11:54 a.m.
NER Named-entity recognition batch_69e64057460c8190962e2e58f06b3985 completed April 20, 2026, 3:03 p.m.
NED1 Entity disambiguation (via context triple) batch_6a089399e7b88190864e9000384d098f completed May 16, 2026, 3:56 p.m.
Created at: April 10, 2026, 1:43 p.m.