Triple

T23259762
Position Surface form Disambiguated ID Type / Status
Subject British sector E581970 entity
Predicate includedBorough P54716 FINISHED
Object Spandau NE NERFINISHED

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: [British sector, includedBorough, Spandau]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Spandau
Context triple: [British sector, includedBorough, 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 historic and culturally vibrant district of Frankfurt am Main, known for its traditional apple wine taverns, museums, and picturesque old town streets.
  • C. Sachsenhausen
    Sachsenhausen is a district or neighborhood within the town of Giengen an der Brenz in the German state of Baden-Württemberg.
  • 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 (2 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_69e246079f58819085eaa9c260906880 completed April 17, 2026, 2:39 p.m.
NER Named-entity recognition batch_69f194c7ec148190b01fd215a0c1daa1 completed April 29, 2026, 5:19 a.m.
Created at: April 17, 2026, 4:11 p.m.