Triple

T12566015
Position Surface form Disambiguated ID Type / Status
Subject Hamburg-Finkenwerder E295478 entity
Predicate integratedInto P77 FINISHED
Object city of Hamburg E7419 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: city of Hamburg | Statement: [Hamburg-Finkenwerder, integratedInto, city of Hamburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: city of Hamburg
Context triple: [Hamburg-Finkenwerder, integratedInto, city of Hamburg]
  • A. Hamburg chosen
    Hamburg is Germany’s second-largest city and a major northern European port and cultural center on the River Elbe.
  • B. Bremen
    Bremen is a city-state in northwestern Germany comprising the cities of Bremen and Bremerhaven, known for its historic Hanseatic heritage and major port on the Weser River.
  • C. Hamburg and Lübeck
    Hamburg and Lübeck is a diocese of the Evangelical Lutheran Church in Northern Germany that encompasses the historic Hanseatic cities of Hamburg and Lübeck and their surrounding regions.
  • D. Gotenhafen
    Gotenhafen was the German name for the port city of Gdynia in occupied Poland during World War II, used as a major naval base by the Kriegsmarine.
  • E. Neustadt, Hamburg
    Neustadt is a central district of Hamburg known for its historic architecture, cultural institutions, and proximity to the city’s main commercial and harbor areas.
  • 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_69d6ad9cac2c81908e8a7bed82d1e21d completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d9549611c081909e611756f3cce7f0 completed April 10, 2026, 7:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69f67c6c21348190b851fce31df307e2 completed May 2, 2026, 10:36 p.m.
Created at: April 8, 2026, 11:49 p.m.