Triple

T16022938
Position Surface form Disambiguated ID Type / Status
Subject Calenberger Neustadt E388647 entity
Predicate partOfUrbanArea P294 FINISHED
Object Hanover metropolitan area E21642 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: Hanover metropolitan area | Statement: [Calenberger Neustadt, partOfUrbanArea, Hanover metropolitan area]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hanover metropolitan area
Context triple: [Calenberger Neustadt, partOfUrbanArea, Hanover metropolitan area]
  • A. Hanover
    Hanover is a small New Hampshire town best known as the home of Dartmouth College, an Ivy League institution.
  • B. Hanover
    Hanover is a small suburban town in Plymouth County, Massachusetts, known for its residential character and local businesses south of Boston.
  • C. Hanover
    Hanover is a small town in South Africa, known as the birthplace of prominent trade unionist Zwelinzima Vavi.
  • D. Hanover chosen
    Hanover is a historic city in northern Germany that served as the capital of the former Kingdom of Hanover and the ancestral seat of the British House of Hanover.
  • E. Hanover
    Hanover is a surname most notably associated with Donna Hanover, an American journalist, actress, and former First Lady of New York City.
  • 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_69d86dada3808190825d5f80d72fbe88 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e18324bf308190b80bb445c7911198 completed April 17, 2026, 12:47 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffdbcfd39c81909bfddfe95f9ad7d2 completed May 10, 2026, 1:13 a.m.
Created at: April 10, 2026, 4:55 a.m.