Triple

T8795245
Position Surface form Disambiguated ID Type / Status
Subject Copa América 1989 E209272 entity
Predicate topScorer P6605 FINISHED
Object Bebeto
Bebeto is a retired Brazilian footballer and prolific striker best known for his successful international career with Brazil, including winning the 1994 FIFA World Cup.
 E759064 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: Bebeto | Statement: [Copa América 1989, topScorer, Bebeto]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Bebeto
Context triple: [Copa América 1989, topScorer, Bebeto]
  • A. Beba
    Beba is a city in Egypt’s Beni Suef Governorate, known as a local administrative and commercial center in the region.
  • B. Bebe
    Bebe is a contemporary women's fashion brand known for its trendy, body-conscious clothing and accessories.
  • C. Bebe
    Bebe is the nickname of Mary “Bebe” Hunt Kemper, a woman known primarily in relation to the Kemper family.
  • D. Piquinho
    Piquinho is the prominent summit cone at the top of Mount Pico in the Azores, known as the highest point in Portugal.
  • E. Bebek
    Bebek is an upscale seaside neighborhood on Istanbul’s Bosphorus shore, known for its scenic views, cafes, and vibrant social life.
  • 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: Bebeto
Triple: [Copa América 1989, topScorer, Bebeto]
Generated description
Bebeto is a retired Brazilian footballer and prolific striker best known for his successful international career with Brazil, including winning the 1994 FIFA World Cup.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Bebeto
Target entity description: Bebeto is a retired Brazilian footballer and prolific striker best known for his successful international career with Brazil, including winning the 1994 FIFA World Cup.
  • A. Beba
    Beba is a city in Egypt’s Beni Suef Governorate, known as a local administrative and commercial center in the region.
  • B. Bebe
    Bebe is a contemporary women's fashion brand known for its trendy, body-conscious clothing and accessories.
  • C. Bebe
    Bebe is the nickname of Mary “Bebe” Hunt Kemper, a woman known primarily in relation to the Kemper family.
  • D. Piquinho
    Piquinho is the prominent summit cone at the top of Mount Pico in the Azores, known as the highest point in Portugal.
  • E. Bebek
    Bebek is an upscale seaside neighborhood on Istanbul’s Bosphorus shore, known for its scenic views, cafes, and vibrant social life.
  • 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_69ca836240888190a62b262e56a69d2f completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc5fa0c6008190a5c4d87510ad5bbd completed March 31, 2026, 11:58 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf6f532ed48190a21996f865428831 completed April 3, 2026, 7:42 a.m.
NEDg Description generation batch_69cf7041b6bc81909924d1382b756746 completed April 3, 2026, 7:46 a.m.
NED2 Entity disambiguation (via description) batch_69cf7163e2088190bf252896cc4036b2 completed April 3, 2026, 7:51 a.m.
Created at: March 30, 2026, 6:43 p.m.