Triple

T20961832
Position Surface form Disambiguated ID Type / Status
Subject Colle di Vespignano E516266 entity
Predicate locatedIn P40 FINISHED
Object Mugello 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: Mugello | Statement: [Colle di Vespignano, locatedIn, Mugello]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mugello
Context triple: [Colle di Vespignano, locatedIn, Mugello]
  • A. Mugello chosen
    Mugello is a historic rural region in northern Tuscany, Italy, known for its rolling hills, medieval villages, and cultural heritage.
  • B. Monza Circuit
    Monza Circuit is a historic Italian motorsport race track, best known as the high-speed home of the Formula One Italian Grand Prix.
  • C. Monza
    Monza is a historic city in northern Italy renowned for its royal villa and the Autodromo Nazionale Monza Formula One racing circuit.
  • D. Livorno Circuit
    Livorno Circuit is a historic Italian motor racing track that has hosted major events including editions of the Italian Grand Prix.
  • E. Autodromo di Modena
    Autodromo di Modena is a historic Italian motor racing circuit near Modena that has hosted various national and international events and served as a key testing venue for local manufacturers like Ferrari and Maserati.
  • 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_69e0b4fde6c48190af1398e7e734629e completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6fb6fd1d48190ad0ec7eb72f84889 completed April 21, 2026, 4:22 a.m.
Created at: April 16, 2026, 1:31 p.m.