Triple

T1808780
Position Surface form Disambiguated ID Type / Status
Subject Veneto E40281 entity
Predicate containsCity P294 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: [Veneto, containsCity, Verona]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Verona
Context triple: [Veneto, containsCity, 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. 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.
  • D. 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.
  • E. Treviso
    Treviso is a historic city in northeastern Italy’s Veneto region, known for its medieval walls, canals, and proximity to Venice.
  • 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_69a88643a3388190a612f2ebe1fb29e7 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa65c310d88190bfd9c27fa238e648 completed March 6, 2026, 5:27 a.m.
NED1 Entity disambiguation (via context triple) batch_69af1f6cb97c8190bf6e8dbdcae3aabd completed March 9, 2026, 7:28 p.m.
Created at: March 4, 2026, 7:32 p.m.