Triple

T3264965
Position Surface form Disambiguated ID Type / Status
Subject Mantua E68502 entity
Predicate nearbyCity P350 FINISHED
Object Cremona E126825 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: Cremona | Statement: [Mantua, nearbyCity, Cremona]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cremona
Context triple: [Mantua, nearbyCity, Cremona]
  • A. Cremona chosen
    Cremona is a historic city in northern Italy renowned for its tradition of violin making and its well-preserved medieval architecture.
  • B. Bergamo
    Bergamo is a historic city in northern Italy known for its medieval walled upper town, rich artistic heritage, and strategic location at the foothills of the Alps.
  • 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 municipality in the Philippine province of Iloilo known for its suburban character and proximity to Iloilo City.
  • E. 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.
  • 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_69ad8590444081909e8107a8aeef3a23 completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adafcb2da08190a7f4fefdfe6d0098 completed March 8, 2026, 5:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69b511f9d8dc8190bc3728e75dec1059 completed March 14, 2026, 7:44 a.m.
Created at: March 8, 2026, 3:09 p.m.