Triple

T1465396
Position Surface form Disambiguated ID Type / Status
Subject Saint Croix E27010 entity
Predicate city P40 FINISHED
Object Christiansted E174746 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: Christiansted | Statement: [Saint Croix, city, Christiansted]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Christiansted
Context triple: [Saint Croix, city, Christiansted]
  • A. Christiansted chosen
    Christiansted is a historic coastal town on the island of Saint Croix in the U.S. Virgin Islands, known for its preserved Danish colonial architecture and waterfront.
  • B. Fredericia
    Fredericia is a Danish coastal town in Jutland known for its historic 17th-century fortress and well-preserved ramparts.
  • C. Kolding
    Kolding is a historic Danish city in Southern Jutland known for Koldinghus Castle, its fjord-side location, and its role as a regional cultural and educational center.
  • D. Esbjerg
    Esbjerg is a major Danish port city on the North Sea, known for its offshore oil and wind industry, maritime heritage, and role as a regional economic center in western Jutland.
  • E. Odense
    Odense is a historic Danish city on the island of Funen, best known as the birthplace of fairy-tale author Hans Christian Andersen and a cultural hub with museums, festivals, and a vibrant literary heritage.
  • 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_69a496d25d6881909dbd84f86d763992 completed March 1, 2026, 7:43 p.m.
NER Named-entity recognition batch_69a4c5bb4e288190997c7e8985e9a2bd completed March 1, 2026, 11:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad36fd91488190b0431bc1c83c64e3 completed March 8, 2026, 8:44 a.m.
Created at: March 1, 2026, 8:01 p.m.