Triple

T1001439
Position Surface form Disambiguated ID Type / Status
Subject Boeing 707 E21610 entity
Predicate notableOperator P179 FINISHED
Object Varig
Varig was Brazil’s former flagship airline, once the country’s largest carrier and a major international operator throughout much of the 20th century.
 E118834 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: Varig | Statement: [Boeing 707, notableOperator, Varig]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Varig
Context triple: [Boeing 707, notableOperator, Varig]
  • A. Varhadi
    Varhadi is a regional dialect of Marathi spoken primarily in the Vidarbha region of Maharashtra, India, known for its distinct phonetic and lexical features.
  • B. Veckring
    Veckring is a small commune in northeastern France, notable for its proximity to the major Maginot Line fortification of Hackenberg.
  • C. Var
    Var is a department in southeastern France known for its Mediterranean coastline, including popular resort areas along the French Riviera.
  • D. Valbo
    Valbo is a locality in Gävleborg County, Sweden, known as the hometown of NHL ice hockey star Nicklas Bäckström.
  • E. Blix
    Blix is a Swedish surname most notably associated with Hans Blix, the former head of the International Atomic Energy Agency and UN weapons inspector.
  • 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: Varig
Triple: [Boeing 707, notableOperator, Varig]
Generated description
Varig was Brazil’s former flagship airline, once the country’s largest carrier and a major international operator throughout much of the 20th century.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Varig
Target entity description: Varig was Brazil’s former flagship airline, once the country’s largest carrier and a major international operator throughout much of the 20th century.
  • A. Varhadi
    Varhadi is a regional dialect of Marathi spoken primarily in the Vidarbha region of Maharashtra, India, known for its distinct phonetic and lexical features.
  • B. Veckring
    Veckring is a small commune in northeastern France, notable for its proximity to the major Maginot Line fortification of Hackenberg.
  • C. Var
    Var is a department in southeastern France known for its Mediterranean coastline, including popular resort areas along the French Riviera.
  • D. Valbo
    Valbo is a locality in Gävleborg County, Sweden, known as the hometown of NHL ice hockey star Nicklas Bäckström.
  • E. Blix
    Blix is a Swedish surname most notably associated with Hans Blix, the former head of the International Atomic Energy Agency and UN weapons inspector.
  • 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_69a493c53e648190ae8cb76c433fd9a7 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b4fcbc04819098d2125518f62ae7 completed March 1, 2026, 9:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac2a1cb4f08190b1351aadd57c3bda completed March 7, 2026, 1:37 p.m.
NEDg Description generation batch_69ac2b0d1b348190b4a34bf1c9b43968 completed March 7, 2026, 1:41 p.m.
NED2 Entity disambiguation (via description) batch_69ac2bb03508819095f791903f048351 completed March 7, 2026, 1:44 p.m.
Created at: March 1, 2026, 7:41 p.m.