Triple

T1721590
Position Surface form Disambiguated ID Type / Status
Subject Nymphenburg Palace E37403 entity
Predicate near P350 FINISHED
Object Munich city center E41138 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: Munich city center | Statement: [Nymphenburg Palace, near, Munich city center]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Munich city center
Context triple: [Nymphenburg Palace, near, Munich city center]
  • A. Munich
    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. Marienplatz (Munich) chosen
    Marienplatz (Munich) is the central square and historic heart of Munich, renowned for its New Town Hall, Glockenspiel, and role as a major cultural and commercial hub.
  • C. 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.
  • D. Cölln
    Cölln was a historic town on the River Spree that, together with Berlin, formed the core of what later became the city of Berlin.
  • E. Regensburg
    Regensburg is a historic city in southeastern Germany known for its well-preserved medieval old town on the Danube River.
  • 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_69a8861acab88190bb43cde203429399 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69aa635703dc8190809260de43b72ea3 completed March 6, 2026, 5:17 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad8aeca12881908efad5991bb0f12b completed March 8, 2026, 2:42 p.m.
Created at: March 4, 2026, 7:30 p.m.