Triple

T2264490
Position Surface form Disambiguated ID Type / Status
Subject Schaumainkai E50113 entity
Predicate partOf P40 FINISHED
Object Frankfurt-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: Frankfurt-Sachsenhausen | Statement: [Schaumainkai, partOf, Frankfurt-Sachsenhausen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Frankfurt-Sachsenhausen
Context triple: [Schaumainkai, partOf, Frankfurt-Sachsenhausen]
  • A. Langenhagen
    Langenhagen is a town in Lower Saxony, Germany, located just north of Hanover and known for hosting part of Hanover Airport and various industrial and commercial facilities.
  • B. Schönefeld
    Schönefeld is a municipality just southeast of Berlin in the German state of Brandenburg, known for hosting the Berlin Brandenburg Airport.
  • C. 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.
  • D. Frankfurt am Main
    Frankfurt am Main is a major German financial and transportation hub on the River Main, known for hosting the European Central Bank and one of Europe’s busiest airports.
  • 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_69a88b01e0048190ba96431b5f990ba9 completed March 4, 2026, 7:41 p.m.
NER Named-entity recognition batch_69abc18ed0708190aa3156e9d35120c3 completed March 7, 2026, 6:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae71cfd3b08190988474aa0fa985fe completed March 9, 2026, 7:08 a.m.
Created at: March 4, 2026, 7:48 p.m.