Triple

T16919743
Position Surface form Disambiguated ID Type / Status
Subject Rathaus Spandau E410413 entity
Predicate locatedInBorough P300 FINISHED
Object Spandau E78225 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: Spandau | Statement: [Rathaus Spandau, locatedInBorough, Spandau]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Spandau
Context triple: [Rathaus Spandau, locatedInBorough, Spandau]
  • A. Spandau chosen
    Spandau is a western borough of Berlin, Germany, known for its historic old town, fortress, and role as an important residential and industrial district.
  • B. Sachsenhausen
    Sachsenhausen is a district or neighborhood within the town of Giengen an der Brenz in the German state of Baden-Württemberg.
  • C. Sachsenhausen
    Sachsenhausen is a historic and culturally vibrant district of Frankfurt am Main, known for its traditional apple wine taverns, museums, and picturesque old town streets.
  • D. Berlin-Moabit
    Berlin-Moabit is a central Berlin neighborhood known for its industrial heritage, diverse population, and proximity to major transport and government districts.
  • E. 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.
  • 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_69d886c7b1e481908c3766dfa8c13458 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3cded2f8481909a20cc08b47e922e completed April 18, 2026, 6:31 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00dc002fac81908b7628d2add1e2dd completed May 10, 2026, 7:26 p.m.
Created at: April 10, 2026, 5:30 a.m.