Triple

T19313819
Position Surface form Disambiguated ID Type / Status
Subject Gandersheim E483040 entity
Predicate hasNearbyCity P350 FINISHED
Object Northeim NE NERFINISHED

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: Northeim | Statement: [Gandersheim, hasNearbyCity, Northeim]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Northeim
Context triple: [Gandersheim, hasNearbyCity, Northeim]
  • A. Northeim chosen
    Northeim is a town in Lower Saxony, Germany, known for its medieval old town and location in the Leine River valley.
  • B. Lüneburg
    Lüneburg is a historic Hanseatic town in northern Germany renowned for its medieval architecture and former wealth from salt mining.
  • C. Lehrte
    Lehrte is a town in Lower Saxony, Germany, located east of Hanover and known historically for its railway junction and agricultural surroundings.
  • D. Stadthagen
    Stadthagen is a historic town in Lower Saxony, Germany, known for its Renaissance architecture and role as an administrative and cultural center of the surrounding region.
  • E. Delmenhorst
    Delmenhorst is a mid-sized industrial and commuter city in northwestern Germany, located near Bremen in the federal state of Lower Saxony.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8e8d04d5c8190baa816986f2b1d1e completed April 10, 2026, 12:10 p.m.
NER Named-entity recognition batch_69e604cfec8c8190a118b327b1418150 completed April 20, 2026, 10:49 a.m.
Created at: April 10, 2026, 1:32 p.m.