Triple

T847535
Position Surface form Disambiguated ID Type / Status
Subject Trollhättan E18308 entity
Predicate hasTwinTown P919 FINISHED
Object Lüneburg E74643 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: Lüneburg | Statement: [Trollhättan, hasTwinTown, Lüneburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lüneburg
Context triple: [Trollhättan, hasTwinTown, Lüneburg]
  • A. Lüneburg chosen
    Lüneburg is a historic Hanseatic town in northern Germany renowned for its medieval architecture and former wealth from salt mining.
  • B. Delmenhorst
    Delmenhorst is a mid-sized industrial and commuter city in northwestern Germany, located near Bremen in the federal state of Lower Saxony.
  • C. 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.
  • D. Hildesheim
    Hildesheim is a historic city in northern Germany renowned for its medieval architecture and UNESCO-listed Romanesque churches.
  • E. Cloppenburg
    Cloppenburg is a rural district in Lower Saxony, Germany, known for its agricultural economy and the open-air museum Museumsdorf Cloppenburg.
  • 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_69a4938b04208190b82e1df6b572c548 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4ac0ba6b4819089c15ed7e1765502 completed March 1, 2026, 9:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69af1f642cf481908028e4947c98e29c completed March 9, 2026, 7:28 p.m.
Created at: March 1, 2026, 7:38 p.m.