Triple

T5208045
Position Surface form Disambiguated ID Type / Status
Subject Neues Rathaus (Munich) E117560 entity
Predicate ownedBy P347 FINISHED
Object City of Munich E21335 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 Munich | Statement: [Neues Rathaus (Munich), ownedBy, City of Munich]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: City of Munich
Context triple: [Neues Rathaus (Munich), ownedBy, City of Munich]
  • A. Munich chosen
    Munich is the capital and largest city of the German state of Bavaria, renowned for its rich cultural scene, historic architecture, and the annual Oktoberfest beer festival.
  • B. Stadt Nürnberg
    Stadt Nürnberg is the municipal government of the German city of Nuremberg, responsible for local administration, public services, and urban infrastructure.
  • C. Regensburg
    Regensburg is a historic city in southeastern Germany known for its well-preserved medieval old town on the Danube River.
  • D. Augsburg
    Augsburg is one of Germany’s oldest cities, a historic Bavarian center known for its rich Renaissance heritage and role as a major medieval trading hub.
  • E. Ingolstadt
    Ingolstadt is a historic city in southern Germany known for its medieval architecture, university tradition, and role as a major hub of the automotive industry.
  • 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_69bd4463dd3c81909966123f20b79d57 completed March 20, 2026, 12:58 p.m.
NER Named-entity recognition batch_69bd7a6d70d081908c74e86b3bca9ba2 completed March 20, 2026, 4:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf857b83f08190b2d575a052198ff1 completed March 22, 2026, 6 a.m.
Created at: March 20, 2026, 1:47 p.m.