Triple

T13519724
Position Surface form Disambiguated ID Type / Status
Subject Romeo and Juliet (1968 film) E322860 entity
Predicate setting P1957 FINISHED
Object Mantua E68502 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: Mantua | Statement: [Romeo and Juliet (1968 film), setting, Mantua]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mantua
Context triple: [Romeo and Juliet (1968 film), setting, Mantua]
  • A. Mantua chosen
    Mantua is a historic city in northern Italy’s Lombardy region, renowned for its Renaissance architecture, artistic heritage, and former status as the seat of the Gonzaga dynasty.
  • B. Mantua
    Mantua is a residential neighborhood in West Philadelphia known for its historic rowhouses and proximity to major universities and cultural institutions.
  • C. Mantua
    Mantua is a small Cuban town and municipality located in the western part of Pinar del Río Province.
  • D. Verona
    Verona is a historic city in northern Italy renowned for its well-preserved Roman architecture and its association with Shakespeare’s "Romeo and Juliet."
  • E. Verona
    Verona is a small borough in Allegheny County, Pennsylvania, situated along the Allegheny River just northeast of Pittsburgh.
  • 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_69f7942668f481909c6d892fdfd32c02 completed May 3, 2026, 6:29 p.m.
Created at: April 9, 2026, 9:44 p.m.