Triple

T19041587
Position Surface form Disambiguated ID Type / Status
Subject Turin, Georgia E466015 entity
Predicate namedAfter P63 FINISHED
Object Turin, Italy 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: Turin, Italy | Statement: [Turin, Georgia, namedAfter, Turin, Italy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Turin, Italy
Context triple: [Turin, Georgia, namedAfter, Turin, Italy]
  • A. Turin chosen
    Turin is a major city in northern Italy known for its rich history, Baroque architecture, automotive industry, and role as a cultural and economic hub.
  • B. Turin
    Turin is a small town located in Coweta County in the U.S. state of Georgia.
  • C. Turin
    Turin is the codename for a generation of AMD EPYC server processors based on the Zen 5 architecture, targeting high-performance and data center workloads.
  • D. Metropolitan City of Turin
    The Metropolitan City of Turin is an Italian administrative region in Piedmont that encompasses the city of Turin and its surrounding municipalities, coordinating local governance, infrastructure, and regional development.
  • E. Piedmont, Italy
    Piedmont, Italy is a northwestern Italian region known for its Alpine landscapes, rich culinary and wine traditions (including Barolo and Barbaresco), and historic cities such as Turin.
  • 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_69d8dd0359648190bc2a9202c5cf29d2 completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5d80118248190af6b4c74df5085ad completed April 20, 2026, 7:38 a.m.
Created at: April 10, 2026, 12:03 p.m.