Triple

T13519723
Position Surface form Disambiguated ID Type / Status
Subject Romeo and Juliet (1968 film) E322860 entity
Predicate setting P1957 FINISHED
Object Verona E118557 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: Verona | Statement: [Romeo and Juliet (1968 film), setting, Verona]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Verona
Context triple: [Romeo and Juliet (1968 film), setting, Verona]
  • A. Verona chosen
    Verona is a historic city in northern Italy renowned for its well-preserved Roman architecture and its association with Shakespeare’s "Romeo and Juliet."
  • B. Verona
    Verona is a small borough in Allegheny County, Pennsylvania, situated along the Allegheny River just northeast of Pittsburgh.
  • C. Padua
    Padua is a historic city in northern Italy renowned as a major cultural and academic center, home to one of Europe’s oldest universities.
  • D. Brescia
    Brescia is a historic industrial and cultural city in northern Italy, known for its Roman and medieval architecture and its role as an economic hub.
  • E. Pavia
    Pavia is a municipality in the Philippine province of Iloilo known for its suburban character and proximity to Iloilo City.
  • 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_69d80766a21881909f21a1b7421d3b8a completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbafa3df0c8190804174695587f0ea completed April 12, 2026, 2:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69f78ad2c4dc819083d23448d21bb0f3 completed May 3, 2026, 5:50 p.m.
Created at: April 9, 2026, 9:44 p.m.