Triple

T9556183
Position Surface form Disambiguated ID Type / Status
Subject Filippo Lippi E230544 entity
Predicate workLocation P7 FINISHED
Object Prato E180335 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: Prato | Statement: [Filippo Lippi, workLocation, Prato]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Prato
Context triple: [Filippo Lippi, workLocation, Prato]
  • A. Prato chosen
    Prato is a historic Tuscan city in central Italy known for its textile industry, medieval architecture, and cultural heritage.
  • B. Scandicci
    Scandicci is a town in central Italy located just southwest of Florence, known as a residential and industrial area within the Tuscan metropolitan region.
  • C. Parma
    Parma is a historic city in northern Italy renowned for its rich artistic heritage, architecture, and culinary traditions, including Parmigiano Reggiano cheese and Parma ham.
  • D. Parma
    Parma is a suburban city located just southwest of Cleveland in northeastern Ohio, known as one of the largest cities in Cuyahoga County.
  • E. 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.
  • 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_69ca847d3be8819099c9dad2a7e786f1 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd9927209c8190bbb494fc9003bc99 completed April 1, 2026, 10:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69d25743261881909b207405d5eaa4cd completed April 5, 2026, 12:36 p.m.
Created at: March 30, 2026, 8:03 p.m.