Triple

T819646
Position Surface form Disambiguated ID Type / Status
Subject Northern Italy E17724 entity
Predicate contains P35 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: [Northern Italy, contains, Verona]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Verona
Context triple: [Northern Italy, contains, 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. 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.
  • C. Pavia
    Pavia is a historic city in northern Italy, known for its ancient university, medieval architecture, and significant role in Lombardy’s cultural and academic life.
  • D. Vicenza
    Vicenza is a historic city in northeastern Italy renowned for its Palladian architecture and cultural heritage.
  • E. Mantua
    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.
  • 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_69a4937bcaac8190a322524ac6f45a5a completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4ab656418819091ecb09e7ede2825 completed March 1, 2026, 9:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69acbf0330b4819089042160ff199336 completed March 8, 2026, 12:12 a.m.
Created at: March 1, 2026, 7:38 p.m.