Triple

T3242347
Position Surface form Disambiguated ID Type / Status
Subject Lake Bolsena E67989 entity
Predicate near P350 FINISHED
Object Orvieto E196077 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: Orvieto | Statement: [Lake Bolsena, near, Orvieto]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Orvieto
Context triple: [Lake Bolsena, near, Orvieto]
  • A. Orvieto chosen
    Orvieto is a historic hilltop city in Umbria, Italy, renowned for its dramatic cliffside setting and magnificent Gothic cathedral.
  • B. Viterbo
    Viterbo is a historic city in central Italy known for its well-preserved medieval center, ancient thermal baths, and role as a papal residence in the 13th century.
  • C. Viterbo
    Viterbo is a municipality in the Caldas Department of Colombia, known for its coffee production and scenic Andean landscapes.
  • D. Montefiascone
    Montefiascone is a historic hilltop town in Italy’s Lazio region, known for its scenic views over Lake Bolsena and its production of the Est! Est!! Est!!! white wine.
  • E. Gubbio
    Gubbio is a historic medieval town in the Umbria region of central Italy, known for its well-preserved stone architecture and traditional festivals.
  • 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_69ad858d27348190abb61c280b4c86a9 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69adaf17463481909447f6ab46016407 completed March 8, 2026, 5:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69b35456814c8190a8e4d53935af539a completed March 13, 2026, 12:03 a.m.
Created at: March 8, 2026, 3:08 p.m.