Triple

T10950568
Position Surface form Disambiguated ID Type / Status
Subject Kaistudio E258714 entity
Predicate cityDistrict P2709 FINISHED
Object HafenCity, Hamburg E899253 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: HafenCity, Hamburg | Statement: [Kaistudio, cityDistrict, HafenCity, Hamburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: HafenCity, Hamburg
Context triple: [Kaistudio, cityDistrict, HafenCity, Hamburg]
  • A. HafenCity, Hamburg chosen
    HafenCity, Hamburg is a major urban redevelopment district on the Elbe River that transforms former port and warehouse areas into a modern mixed-use waterfront quarter.
  • B. Ohlsdorf, Hamburg
    Ohlsdorf, Hamburg is a northern district of Hamburg, Germany, best known for containing one of the world’s largest rural cemeteries, Ohlsdorf Cemetery.
  • C. Port of Hamburg
    The Port of Hamburg is Germany’s largest seaport and a major European logistics hub, known as the country’s “Gateway to the World.”
  • D. Lübeck seaport
    Lübeck seaport is a major Baltic Sea harbor in northern Germany that serves as an important hub for maritime trade and ferry connections between Central Europe and Scandinavia.
  • E. Duisburg Inner Harbour
    Duisburg Inner Harbour is a historic inland port area in Duisburg, Germany, that has been transformed into a mixed-use district with cultural, residential, and commercial developments along the waterfront.
  • 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_69d6aa88500c819097d7032ca578e74f completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d770ed2f1c819081ec58457f57889d completed April 9, 2026, 9:27 a.m.
NED1 Entity disambiguation (via context triple) batch_69e3a94466bc8190b4d4db70083ab5a5 completed April 18, 2026, 3:54 p.m.
Created at: April 8, 2026, 9:23 p.m.