Triple

T10306151
Position Surface form Disambiguated ID Type / Status
Subject Annette Kurschus E241765 entity
Predicate workLocation P7 FINISHED
Object Hannover 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: Hannover | Statement: [Annette Kurschus, workLocation, Hannover]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hannover
Context triple: [Annette Kurschus, workLocation, Hannover]
  • A. 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.
  • B. Braunschweig
    Braunschweig is a historic city in northern Germany known for its medieval architecture, cultural institutions, and role as an important economic and scientific center.
  • C. Hanover
    Hanover is a small New Hampshire town best known as the home of Dartmouth College, an Ivy League institution.
  • D. Hanover
    Hanover is a small suburban town in Plymouth County, Massachusetts, known for its residential character and local businesses south of Boston.
  • E. 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.
  • 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_69d381ac38808190a8ca7457c85b625b completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4d30a6c888190acdd0a645247736a completed April 7, 2026, 9:48 a.m.
NED1 Entity disambiguation (via context triple) batch_69d9336139088190bd9ea3e2333c59cb completed April 10, 2026, 5:29 p.m.
Created at: April 6, 2026, 11:46 a.m.