Triple

T5991730
Position Surface form Disambiguated ID Type / Status
Subject Bergedorf E133365 entity
Predicate locatedIn P40 FINISHED
Object 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: Hamburg | Statement: [Bergedorf, locatedIn, Hamburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hamburg
Context triple: [Bergedorf, locatedIn, 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. 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.
  • D. Lübeck
    Lübeck is a historic Hanseatic city in northern Germany renowned for its medieval architecture and long-standing role as a key trading hub on the Baltic Sea.
  • E. Bremerhaven
    Bremerhaven is a major German port city on the North Sea, known for its maritime industry, shipbuilding, and role as a key hub for trade and logistics.
  • 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_69c0087010d081908bb8142342d63330 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c04e8fd030819095a4f3b3d425ec21 completed March 22, 2026, 8:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69c124f45d8c8190a757c82abd85c514 completed March 23, 2026, 11:33 a.m.
Created at: March 22, 2026, 4:05 p.m.