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.