Triple

T25697331
Position Surface form Disambiguated ID Type / Status
Subject Vinchio E644356 entity
Predicate hasWineType P2082 FINISHED
Object Piemonte DOC wines
Piemonte DOC wines are Italian appellation wines from the Piedmont region, known for their diverse styles and high quality, often made from native grape varieties such as Barbera, Dolcetto, and Nebbiolo.
E1689269 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: Piemonte DOC wines | Statement: [Vinchio, hasWineType, Piemonte DOC wines]
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: Piemonte DOC wines
Triple: [Vinchio, hasWineType, Piemonte DOC wines]
Generated description
Piemonte DOC wines are Italian appellation wines from the Piedmont region, known for their diverse styles and high quality, often made from native grape varieties such as Barbera, Dolcetto, and Nebbiolo.

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_69e77e82c9bc8190893090b2f6c64f1d completed April 21, 2026, 1:41 p.m.
NER Named-entity recognition batch_69f5fbc4f1e88190bd5b195d92e44d3e completed May 2, 2026, 1:27 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10c16a501c81908a42ec8e825566d0 completed May 22, 2026, 8:49 p.m.
NEDg Description generation batch_6a10c2489ee48190a35add76cdf94b8b completed May 22, 2026, 8:53 p.m.
NED2 Entity disambiguation (via description) batch_6a10c30975b08190b22c052dab147afe completed May 22, 2026, 8:56 p.m.
Created at: April 21, 2026, 8:38 p.m.