Triple

T22435412
Position Surface form Disambiguated ID Type / Status
Subject Ortsbezirk Süd E554608 entity
Predicate hasPart P35 FINISHED
Object Sachsenhausen 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: Sachsenhausen | Statement: [Ortsbezirk Süd, hasPart, Sachsenhausen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sachsenhausen
Context triple: [Ortsbezirk Süd, hasPart, Sachsenhausen]
  • A. Sachsenhausen
    Sachsenhausen is a district or neighborhood within the town of Giengen an der Brenz in the German state of Baden-Württemberg.
  • B. Sachsenhausen chosen
    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. Hellersdorf
    Hellersdorf is a locality in eastern Berlin, Germany, known for its large prefabricated housing estates built during the GDR era.
  • D. Spandau
    Spandau is a western borough of Berlin, Germany, known for its historic old town, fortress, and role as an important residential and industrial district.
  • E. Sachsenhausen district
    The Sachsenhausen district is a historic quarter of Frankfurt am Main, Germany, known for its traditional apple wine taverns, cobbled streets, and vibrant nightlife along the south bank of the Main River.
  • 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_69e11e5010e48190ae1e9c9db9697637 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f15adda0e48190825a5b705ae52d5b completed April 29, 2026, 1:11 a.m.
Created at: April 16, 2026, 8:47 p.m.