Triple

T2060379
Position Surface form Disambiguated ID Type / Status
Subject Museumsufer Frankfurt E45774 entity
Predicate cityDistrict P2709 FINISHED
Object Sachsenhausen E131173 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: Sachsenhausen | Statement: [Museumsufer Frankfurt, cityDistrict, Sachsenhausen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sachsenhausen
Context triple: [Museumsufer Frankfurt, cityDistrict, Sachsenhausen]
  • A. 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.
  • B. 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.
  • C. Lichterfelde
    Lichterfelde is a residential district in southwestern Berlin known for its historic villas, leafy streets, and affluent character.
  • D. Schönhausen
    Schönhausen is a village in Saxony-Anhalt, Germany, best known as the birthplace of 19th-century statesman Otto von Bismarck.
  • E. Wilmersdorf
    Wilmersdorf is a residential district in southwestern Berlin known for its affluent neighborhoods, shopping streets like Kurfürstendamm, and a mix of historic and modern architecture.
  • 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_69a8891a19508190a12ef1e192308dcb completed March 4, 2026, 7:33 p.m.
NER Named-entity recognition batch_69abb9cfdac88190b8b7af1bfea6a78e completed March 7, 2026, 5:38 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae652a87748190b9e18356ffb13bed completed March 9, 2026, 6:14 a.m.
Created at: March 4, 2026, 7:40 p.m.