Triple
T13711553
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1999 Copa América |
E328782
|
entity |
| Predicate | bestGoalkeeper |
P14676
|
FINISHED |
| Object | Dida |
E950259
|
NE FINISHED |
How this triple was built (2 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: Dida | Statement: [1999 Copa América, bestGoalkeeper, Dida]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dida Context triple: [1999 Copa América, bestGoalkeeper, Dida]
-
A.
Dida
chosen
Dida is a Brazilian former professional goalkeeper best known for his time at AC Milan, where he was regarded as one of the world’s top keepers in the early 2000s.
-
B.
Djanira
Djanira was a prominent Brazilian modernist painter known for her vivid depictions of everyday life, religious themes, and popular culture.
-
C.
Didi
Didi was a legendary Brazilian attacking midfielder, renowned for his playmaking brilliance and key role in Brazil’s World Cup victories in 1958 and 1962.
-
D.
Didi Petet
Didi Petet was a prominent Indonesian actor and comedian best known for his roles in popular films and television series from the 1980s and 1990s.
-
E.
Vavá
Vavá was a prolific Brazilian striker renowned for scoring in two consecutive World Cup finals and helping Brazil win the 1958 and 1962 tournaments.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d80770b9bc81909f70c8c317d53cff |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dd4395e8c0819098719c8cd344aa33 |
completed | April 13, 2026, 7:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f79d54a68081908df25edf6d5df362 |
completed | May 3, 2026, 7:09 p.m. |
Created at: April 9, 2026, 9:54 p.m.