Triple

T6307081
Position Surface form Disambiguated ID Type / Status
Subject Ronaldinho E141403 entity
Predicate alsoKnownAs P39 FINISHED
Object R10
R10 is a nickname for Ronaldinho, the legendary Brazilian attacking midfielder and forward renowned for his flair, creativity, and skillful play.
E585245 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: R10 | Statement: [Ronaldinho, alsoKnownAs, R10]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: R10
Context triple: [Ronaldinho, alsoKnownAs, R10]
  • A. R103
    R103 is a regional road in South Africa that serves as an alternative route to the N3, connecting towns such as Ladysmith along the KwaZulu-Natal corridor.
  • B. R11
    R11 is the internal station code used by the New York City Subway for the Grand Central–42nd Street complex in Midtown Manhattan.
  • C. R 104
    R 104 is the hull identification number assigned to the NOAA research vessel Ronald H. Brown, a major U.S. oceanographic and atmospheric research ship.
  • D. R107
    R107 is the long-running second generation of the Mercedes-Benz SL roadster, produced from the early 1970s to late 1980s and renowned for its durability, safety, and classic styling.
  • E. R5
    R5 is a government office building in Oslo that forms part of Norway’s central Regjeringskvartalet complex.
  • 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: R10
Triple: [Ronaldinho, alsoKnownAs, R10]
Generated description
R10 is a nickname for Ronaldinho, the legendary Brazilian attacking midfielder and forward renowned for his flair, creativity, and skillful play.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: R10
Target entity description: R10 is a nickname for Ronaldinho, the legendary Brazilian attacking midfielder and forward renowned for his flair, creativity, and skillful play.
  • A. R103
    R103 is a regional road in South Africa that serves as an alternative route to the N3, connecting towns such as Ladysmith along the KwaZulu-Natal corridor.
  • B. R11
    R11 is the internal station code used by the New York City Subway for the Grand Central–42nd Street complex in Midtown Manhattan.
  • C. R 104
    R 104 is the hull identification number assigned to the NOAA research vessel Ronald H. Brown, a major U.S. oceanographic and atmospheric research ship.
  • D. R107
    R107 is the long-running second generation of the Mercedes-Benz SL roadster, produced from the early 1970s to late 1980s and renowned for its durability, safety, and classic styling.
  • E. R5
    R5 is the U.S. Forest Service’s Pacific Southwest Region, which oversees national forests primarily in California and parts of neighboring areas.
  • 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_69c008d00efc8190a36c05b4b4a3bf4b completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c0647b69f08190bb085f9b700f6453 completed March 22, 2026, 9:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69c5e44c11f48190a8c3c36172cd8da0 completed March 27, 2026, 1:58 a.m.
NEDg Description generation batch_69c5edd0f9348190a17d00f402e2cdad completed March 27, 2026, 2:39 a.m.
NED2 Entity disambiguation (via description) batch_69c5ee31ea6881908e30911ccf447400 completed March 27, 2026, 2:40 a.m.
Created at: March 22, 2026, 4:28 p.m.