Triple

T17111090
Position Surface form Disambiguated ID Type / Status
Subject Hamburg America Line E415224 entity
Predicate servedPort P1763 FINISHED
Object Cuxhaven E238867 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: Cuxhaven | Statement: [Hamburg America Line, servedPort, Cuxhaven]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cuxhaven
Context triple: [Hamburg America Line, servedPort, Cuxhaven]
  • A. Cuxhaven chosen
    Cuxhaven is a German port city on the North Sea coast that historically served as an important naval and maritime hub.
  • B. Wilhelmshaven
    Wilhelmshaven is a coastal city in northwestern Germany known for its major naval base and port on the North Sea.
  • C. Port of Cuxhaven
    The Port of Cuxhaven is a German seaport on the North Sea known as an important hub for offshore wind energy, RoRo traffic, and maritime logistics.
  • D. Itzehoe
    Itzehoe is a historic town in northern Germany known for its medieval origins and role as a regional center in the state of Schleswig-Holstein.
  • E. Aurich
    Aurich is a historic town in northwestern Germany that serves as one of the principal urban centers of the East Frisia region in Lower Saxony.
  • 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_69d886d090cc8190a39cb94992586905 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3dc2a7f2c81908eb19594b6accab7 completed April 18, 2026, 7:31 p.m.
NED1 Entity disambiguation (via context triple) batch_6a013a062d7c81908fe8cdc9e4637168 completed May 11, 2026, 2:08 a.m.
Created at: April 10, 2026, 5:35 a.m.