Triple

T3067145
Position Surface form Disambiguated ID Type / Status
Subject Tom Stoppard E62129 entity
Predicate wrote P2831 FINISHED
Object Leopoldstadt E276789 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: Leopoldstadt | Statement: [Tom Stoppard, wrote, Leopoldstadt]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Leopoldstadt
Context triple: [Tom Stoppard, wrote, Leopoldstadt]
  • A. Leopoldstadt chosen
    Leopoldstadt is Vienna’s second municipal district, known for encompassing the Prater park and its historic Jewish quarter.
  • B. The Shtetl
    The Shtetl is a work by Yiddish writer Sholem Asch that vividly portrays the life, culture, and struggles of Eastern European Jewish small-town communities.
  • C. Tales from the Vienna Woods
    Tales from the Vienna Woods is a famous waltz by Johann Strauss II that evokes the charm and atmosphere of the Viennese countryside.
  • D. Het Hogeland
    Het Hogeland is a coastal municipality in the northern Netherlands known for its open landscapes, historic villages, and Wadden Sea shoreline.
  • E. Goodbye to Berlin
    Goodbye to Berlin is a semi-autobiographical novel by Christopher Isherwood that portrays the lives of diverse characters in pre-World War II Berlin and later inspired the musical Cabaret.
  • 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_69ad85793e5c8190a358049bc4a98d8c completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada0fea06881909e5251eea26599ac completed March 8, 2026, 4:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69b1f87bec6c8190ae928fc7c4467859 completed March 11, 2026, 11:19 p.m.
Created at: March 8, 2026, 3:02 p.m.