Triple

T7780803
Position Surface form Disambiguated ID Type / Status
Subject Jade Bight E221509 entity
Predicate borderedBy P224 FINISHED
Object Wilhelmshaven E41561 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: Wilhelmshaven | Statement: [Jade Bight, borderedBy, Wilhelmshaven]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wilhelmshaven
Context triple: [Jade Bight, borderedBy, Wilhelmshaven]
  • A. Wilhelmshaven chosen
    Wilhelmshaven is a coastal city in northwestern Germany known for its major naval base and port on the North Sea.
  • B. Cuxhaven
    Cuxhaven is a German port city on the North Sea coast that historically served as an important naval and maritime hub.
  • C. Dover
    Dover is a small town in eastern Dutchess County, New York, known for its rural character and location near the Connecticut border.
  • D. Dover
    Dover is one of the oldest permanent European settlements in what is now the U.S. state of New Hampshire, historically significant as an early colonial town and port.
  • E. Dover
    Dover is a coastal town in southeast England best known for its white chalk cliffs and its strategic port facing the narrowest part of the English Channel.
  • 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_69ca83ebbef881909ac47f789145fef7 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69caa4d6cf9881909f5220437db13cc7 completed March 30, 2026, 4:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69caf5c1f20c81908ae2fc35550b91b5 completed March 30, 2026, 10:14 p.m.
Created at: March 30, 2026, 4:20 p.m.