Triple

T12644396
Position Surface form Disambiguated ID Type / Status
Subject Maipú E301983 entity
Predicate grapeVariety P975 FINISHED
Object Torrontés E342852 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: Torrontés | Statement: [Maipú, grapeVariety, Torrontés]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Torrontés
Context triple: [Maipú, grapeVariety, Torrontés]
  • A. Torrontés chosen
    Torrontés is an aromatic white wine grape variety from Argentina, known for its floral, citrusy wines with crisp acidity.
  • B. Serón
    Serón is a small rural settlement located in the Río Hurtado area of northern Chile, known for its Andean landscapes and agricultural surroundings.
  • C. Almagro
    Almagro is a Spanish surname borne by various notable figures, including politicians, athletes, and artists from Spanish-speaking countries.
  • D. Almagro
    Almagro is a traditional middle-class neighborhood in central Buenos Aires, Argentina, known for its historic tango culture, cafes, and densely populated residential streets.
  • E. Gualba
    Gualba is a small municipality in the Vallès Oriental comarca of Catalonia, Spain, known for its natural surroundings near the Montseny Massif.
  • 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_69d7bdec9f9c8190b4bac675b7588211 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d9614bf2f881909976becdf747f4fb completed April 10, 2026, 8:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6719a45d881908dd895836a225781 completed May 2, 2026, 9:50 p.m.
Created at: April 9, 2026, 5:17 p.m.