Triple

T1794607
Position Surface form Disambiguated ID Type / Status
Subject Montserrat Caballé E39574 entity
Predicate performedAt P270 FINISHED
Object Vienna State Opera E17596 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: Vienna State Opera | Statement: [Montserrat Caballé, performedAt, Vienna State Opera]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vienna State Opera
Context triple: [Montserrat Caballé, performedAt, Vienna State Opera]
  • A. Vienna State Opera chosen
    The Vienna State Opera is one of the world’s leading opera houses, renowned for its rich history, prestigious productions, and central role in Vienna’s classical music culture.
  • B. Burgtheater, Vienna
    The Burgtheater in Vienna is Austria’s historic national theatre and one of the most prestigious and influential German-language stages in the world.
  • C. Berlin State Opera
    The Berlin State Opera is one of Germany’s leading opera houses, renowned for its historic venue, world-class productions, and prominent role in Berlin’s cultural life.
  • D. Bavarian State Opera
    The Bavarian State Opera is one of Germany’s leading opera companies, renowned for its world-class productions and historic home at Munich’s National Theatre.
  • E. Opernhaus
    Opernhaus is an underground rapid transit station on the Nuremberg U-Bahn network in Nuremberg, Germany.
  • 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_69a88631854081909723959921e45c2b completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa653daa0c8190a5d96c20c8a0af15 completed March 6, 2026, 5:25 a.m.
NED1 Entity disambiguation (via context triple) batch_69adc9a4ee9c8190a6cdb5df16a48711 completed March 8, 2026, 7:10 p.m.
Created at: March 4, 2026, 7:32 p.m.