Triple

T9630529
Position Surface form Disambiguated ID Type / Status
Subject Hamburg-Mitte E232790 entity
Predicate contains P35 FINISHED
Object Finkenwerder E804139 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: Finkenwerder | Statement: [Hamburg-Mitte, contains, Finkenwerder]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Finkenwerder
Context triple: [Hamburg-Mitte, contains, Finkenwerder]
  • A. Hamburg-Finkenwerder chosen
    Hamburg-Finkenwerder is a district of Hamburg, Germany, known for its historic and ongoing role in shipbuilding and aviation industries along the River Elbe.
  • B. Fuhlsbüttel
    Fuhlsbüttel is a district in the northern German city of Hamburg best known for hosting the city’s international airport.
  • C. Elmshorn
    Elmshorn is a town in northern Germany’s Schleswig-Holstein state, known as an industrial and commuter hub northwest of Hamburg.
  • D. Brunsbüttel
    Brunsbüttel is a German port town at the western entrance of the Kiel Canal on the North Sea coast of Schleswig-Holstein.
  • E. Falkensee
    Falkensee is a town in the Havelland district of Brandenburg, Germany, situated just west of Berlin and functioning largely as a residential suburb of the capital.
  • 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_69ca848940cc8190b97cec654cb3bb4a completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd9b01863c8190a9ec4684804f96bc completed April 1, 2026, 10:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69d189f7ea448190b9fe123589a9f3c5 completed April 4, 2026, 10 p.m.
Created at: March 30, 2026, 8:11 p.m.