Triple

T299481
Position Surface form Disambiguated ID Type / Status
Subject Frauenliebe und -leben E6166 entity
Predicate firstPerformanceLocation P128 FINISHED
Object Leipzig E38199 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: Leipzig | Statement: [Frauenliebe und -leben, firstPerformanceLocation, Leipzig]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Leipzig
Context triple: [Frauenliebe und -leben, firstPerformanceLocation, Leipzig]
  • A. Leipzig chosen
    Leipzig is a major city in eastern Germany known for its rich cultural heritage, vibrant music and arts scene, and important role in trade and commerce.
  • B. Dresden
    Dresden is a historic cultural and economic center in eastern Germany, renowned for its baroque architecture, art collections, and reconstruction after World War II.
  • C. Chemnitz
    Chemnitz is a city in eastern Germany known for its industrial heritage and post-reunification urban redevelopment.
  • D. Rostock
    Rostock is a historic Hanseatic city in northern Germany known for its significant seaport on the Baltic Sea and its long maritime and trading tradition.
  • E. Lübeck
    Lübeck is a historic Hanseatic city in northern Germany renowned for its medieval architecture and long-standing role as a key trading hub on the Baltic Sea.
  • 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_69a2e79114b081909490b3bf5a5dbb51 completed Feb. 28, 2026, 1:03 p.m.
NER Named-entity recognition batch_69a2e9e53b2c81909c4a15b366d94cd6 completed Feb. 28, 2026, 1:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69a7edf1c89c819090d188f2990388b1 completed March 4, 2026, 8:31 a.m.
Created at: Feb. 28, 2026, 1:06 p.m.