Triple

T3259782
Position Surface form Disambiguated ID Type / Status
Subject Province of Viterbo E68380 entity
Predicate borders P224 FINISHED
Object Umbria E35541 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: Umbria | Statement: [Province of Viterbo, borders, Umbria]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Umbria
Context triple: [Province of Viterbo, borders, Umbria]
  • A. Umbria chosen
    Umbria is a central Italian region known for its historic hill towns, medieval architecture, and rich cultural heritage.
  • B. La Marche
    La Marche is a historic province in central France known for its rural landscapes and role as a frontier region between major medieval territories.
  • C. Abruzzo
    Abruzzo is a central Italian region known for its rugged Apennine mountains, national parks, and Adriatic Sea coastline.
  • D. Emilia-Romagna
    Emilia-Romagna is a region in northern Italy known for its rich culinary traditions, historic cities, and strong industrial and agricultural economy.
  • E. Maremma region
    The Maremma region is a coastal area of southwestern Tuscany and northern Lazio in Italy, known for its wild landscapes, medieval hill towns, and traditional agriculture.
  • 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_69ad858f74408190bcbd07f967cd7bd0 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69adafa673a481909b5024b4e0e1c2a7 completed March 8, 2026, 5:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69bdf9e0af508190adbd16c718d996e2 completed March 21, 2026, 1:52 a.m.
Created at: March 8, 2026, 3:09 p.m.