Triple

T5058794
Position Surface form Disambiguated ID Type / Status
Subject Monterosa Ski E113972 entity
Predicate hasValley P650 FINISHED
Object Valsesia
Valsesia is a scenic alpine valley in Italy’s Piedmont region, known for its mountain landscapes, outdoor sports, and traditional villages.
 E492736 NE FINISHED

How this triple was built (4 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: Valsesia | Statement: [Monterosa Ski, hasValley, Valsesia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Valsesia
Context triple: [Monterosa Ski, hasValley, Valsesia]
  • A. Monferrato
    Monferrato is a historic hilly wine-producing region in northwestern Italy, renowned for its vineyards, medieval towns, and cultural landscapes.
  • B. Tuscany
    Tuscany is a central Italian region renowned for its rolling landscapes, historic cities like Florence and Siena, and its pivotal role in art, culture, and the birth of the Renaissance.
  • C. Liguria
    Liguria is a coastal region in northwestern Italy known for its picturesque Riviera, including the Cinque Terre and the city of Genoa.
  • D. Umbria
    Umbria is a central Italian region known for its historic hill towns, medieval architecture, and rich cultural heritage.
  • E. Senigallia
    Senigallia is a historic coastal town in Italy’s Marche region, known for its Adriatic seaside resort, Renaissance heritage, and well-preserved old town.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Valsesia
Triple: [Monterosa Ski, hasValley, Valsesia]
Generated description
Valsesia is a scenic alpine valley in Italy’s Piedmont region, known for its mountain landscapes, outdoor sports, and traditional villages.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Valsesia
Target entity description: Valsesia is a scenic alpine valley in Italy’s Piedmont region, known for its mountain landscapes, outdoor sports, and traditional villages.
  • A. Monferrato
    Monferrato is a historic hilly wine-producing region in northwestern Italy, renowned for its vineyards, medieval towns, and cultural landscapes.
  • B. Tuscany
    Tuscany is a central Italian region renowned for its rolling landscapes, historic cities like Florence and Siena, and its pivotal role in art, culture, and the birth of the Renaissance.
  • C. Liguria
    Liguria is a coastal region in northwestern Italy known for its picturesque Riviera, including the Cinque Terre and the city of Genoa.
  • D. Umbria
    Umbria is a central Italian region known for its historic hill towns, medieval architecture, and rich cultural heritage.
  • E. Senigallia
    Senigallia is a historic coastal town in Italy’s Marche region, known for its Adriatic seaside resort, Renaissance heritage, and well-preserved old town.
  • F. None of above. chosen

Provenance (5 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_69bd443aa1f88190abb992d138f2cf42 completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd74523434819092b8b15992073b5b completed March 20, 2026, 4:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69beb102bb348190b235e4adb7a3f88b completed March 21, 2026, 2:53 p.m.
NEDg Description generation batch_69beb407ec7881909e3643c1fd162568 completed March 21, 2026, 3:06 p.m.
NED2 Entity disambiguation (via description) batch_69beb4843b588190947b3c2ae7709e67 completed March 21, 2026, 3:08 p.m.
Created at: March 20, 2026, 1:38 p.m.