Triple

T19799489
Position Surface form Disambiguated ID Type / Status
Subject Luino E475631 entity
Predicate hasNearbyCity P350 FINISHED
Object Varese NE NERFINISHED

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: Varese | Statement: [Luino, hasNearbyCity, Varese]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Varese
Context triple: [Luino, hasNearbyCity, Varese]
  • A. Varese chosen
    Varese is a city in northern Italy known for its lakeside setting, surrounding Prealps, and role as an important economic and cultural center in the Lombardy region.
  • B. Lecco
    Lecco is an Italian town in the Lombardy region, known for its scenic location at the southeastern tip of Lake Como and its surrounding Alpine foothills.
  • C. Busto Arsizio
    Busto Arsizio is an industrial city in the Lombardy region of northern Italy, known for its textile and manufacturing heritage and its location within the greater Milan metropolitan area.
  • D. Legnano
    Legnano is a town in the Lombardy region of northern Italy, historically known for its medieval Battle of Legnano and its industrial development.
  • E. 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.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8e51b014081908b263e167370529a completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e653c930a08190a2263db7170edd71 completed April 20, 2026, 4:26 p.m.
Created at: April 10, 2026, 1:49 p.m.