Triple

T172071
Position Surface form Disambiguated ID Type / Status
Subject Duchy of Savoy E3497 entity
Predicate capital P234 FINISHED
Object Turin E15144 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: Turin | Statement: [Duchy of Savoy, capital, Turin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Turin
Context triple: [Duchy of Savoy, capital, Turin]
  • 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. Milan
    Milan is a major Italian metropolis renowned as a global center for fashion, design, finance, and culture.
  • C. Bologna
    Bologna is a historic city in northern Italy renowned for its medieval architecture, rich culinary tradition, and the University of Bologna, one of the oldest universities in the world.
  • D. Genoa
    Genoa is a historic port city in northwestern Italy known for its significant maritime heritage, trade, and role as a major economic hub on the Ligurian coast.
  • E. Naples
    Naples is a historic coastal city in southern Italy renowned for its rich cultural heritage, vibrant street life, and as the birthplace of pizza.
  • 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_69a25374990081909766d30c79a18e0e completed Feb. 28, 2026, 2:31 a.m.
NER Named-entity recognition batch_69a258b94d00819098e90bdfa1306f9f completed Feb. 28, 2026, 2:53 a.m.
NED1 Entity disambiguation (via context triple) batch_69a35e9fb768819087282451ec8051b0 completed Feb. 28, 2026, 9:31 p.m.
Created at: Feb. 28, 2026, 2:39 a.m.