Triple

T14470412
Position Surface form Disambiguated ID Type / Status
Subject Braunschweig Palace E358823 entity
Predicate owner P347 FINISHED
Object City of Braunschweig E72622 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 Braunschweig | Statement: [Braunschweig Palace, owner, City of Braunschweig]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: City of Braunschweig
Context triple: [Braunschweig Palace, owner, City of Braunschweig]
  • A. Braunschweig chosen
    Braunschweig is a historic city in northern Germany known for its medieval architecture, cultural institutions, and role as an important economic and scientific center.
  • B. Neubrandenburg
    Neubrandenburg is a historic city in northeastern Germany known for its well-preserved medieval brick Gothic architecture and distinctive city wall with multiple gate towers.
  • C. Hildesheim
    Hildesheim is a historic city in northern Germany renowned for its medieval architecture and UNESCO-listed Romanesque churches.
  • 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. Wolfenbüttel
    Wolfenbüttel is a historic town in Lower Saxony, Germany, known for its Renaissance castle and rich cultural heritage.
  • 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_69d827966698819082e140837737501d completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de91f969788190a5114f92d7159aae completed April 14, 2026, 7:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd7a3bade48190a4608ca46f5f558a completed May 8, 2026, 5:52 a.m.
Created at: April 10, 2026, 1:20 a.m.