Triple

T8534967
Position Surface form Disambiguated ID Type / Status
Subject Vinho Verde region E202054 entity
Predicate subregion P747 FINISHED
Object Baião
Baião is a Portuguese wine subregion within Vinho Verde, known for producing fresh, aromatic white wines, often from the Avesso grape.
 E740608 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: Baião | Statement: [Vinho Verde region, subregion, Baião]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Baião
Context triple: [Vinho Verde region, subregion, Baião]
  • A. Santana de Parnaíba
    Santana de Parnaíba is a historic municipality in the São Paulo metropolitan region of Brazil, known for its well-preserved colonial architecture and cultural heritage.
  • B. Arapiraca
    Arapiraca is a major city in the Brazilian state of Alagoas, known as an important regional commercial and agricultural center.
  • C. Caieiras
    Caieiras is a municipality in the metropolitan region of São Paulo, Brazil, known for its industrial activity and surrounding green areas.
  • D. Beberibe
    Beberibe is a coastal municipality in northeastern Brazil known for its scenic beaches, sand dunes, and colorful cliffs.
  • E. Icó
    Icó is a historic municipality in northeastern Brazil known for its colonial architecture and cultural heritage within the state of Ceará.
  • 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: Baião
Triple: [Vinho Verde region, subregion, Baião]
Generated description
Baião is a Portuguese wine subregion within Vinho Verde, known for producing fresh, aromatic white wines, often from the Avesso grape.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Baião
Target entity description: Baião is a Portuguese wine subregion within Vinho Verde, known for producing fresh, aromatic white wines, often from the Avesso grape.
  • A. Santana de Parnaíba
    Santana de Parnaíba is a historic municipality in the São Paulo metropolitan region of Brazil, known for its well-preserved colonial architecture and cultural heritage.
  • B. Arapiraca
    Arapiraca is a major city in the Brazilian state of Alagoas, known as an important regional commercial and agricultural center.
  • C. Caieiras
    Caieiras is a municipality in the metropolitan region of São Paulo, Brazil, known for its industrial activity and surrounding green areas.
  • D. Beberibe
    Beberibe is a coastal municipality in northeastern Brazil known for its scenic beaches, sand dunes, and colorful cliffs.
  • E. Icó
    Icó is a historic municipality in northeastern Brazil known for its colonial architecture and cultural heritage within the state of Ceará.
  • 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_69ca832355b08190b8b6a4ab4a4a3554 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe6a295c88190a432a060ee73f04e completed March 31, 2026, 3:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce6d8332cc819083c86e0dc58bcc37 completed April 2, 2026, 1:22 p.m.
NEDg Description generation batch_69ce6f02fabc819093b95e0d318c5649 completed April 2, 2026, 1:28 p.m.
NED2 Entity disambiguation (via description) batch_69ce6f9b12148190be55b3f5f2b518d9 completed April 2, 2026, 1:31 p.m.
Created at: March 30, 2026, 6:17 p.m.