Triple

T15247911
Position Surface form Disambiguated ID Type / Status
Subject Lüneburg Town Hall E364434 entity
Predicate ownedBy P347 FINISHED
Object City of 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: City of Lüneburg | Statement: [Lüneburg Town Hall, ownedBy, City of Lüneburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: City of Lüneburg
Context triple: [Lüneburg Town Hall, ownedBy, City of 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. Hildesheim
    Hildesheim is a historic city in northern Germany renowned for its medieval architecture and UNESCO-listed Romanesque churches.
  • 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. Halberstadt
    Halberstadt is a historic town in the German state of Saxony-Anhalt, known for its medieval architecture and role as a former episcopal seat.
  • E. Bückeburg
    Bückeburg is a historic town in Lower Saxony, Germany, known for its former role as the residence of the Counts and Princes of Schaumburg-Lippe and its well-preserved Renaissance castle.
  • 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_69d85a0dde7481908fc64d1e82d5d20d completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e007f4f9d48190b96a7e0c6993cd69 completed April 15, 2026, 9:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69ff2ce894548190a19bab33285ad165 completed May 9, 2026, 12:47 p.m.
Created at: April 10, 2026, 3:13 a.m.