Triple
T29887937
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Copa América 1987 |
E759066
|
entity |
| Predicate | topScorerCountry |
P168212
|
FINISHED |
| Object | Colombia |
—
|
NE NERFINISHED |
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: Colombia | Statement: [Copa América 1987, topScorerCountry, Colombia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: topScorerCountry Context triple: [Copa América 1987, topScorerCountry, Colombia]
-
A.
topScorer
Indicates that the subject is the individual with the highest score among a specified group or in a particular context.
-
B.
topRankedScoreCountry
Indicates that a country has the highest (top-ranked) score among a set of countries according to a specified scoring metric.
-
C.
countryOfLeagueTopScorerTitle
Indicates the country associated with the league’s top scorer title.
-
D.
topScorerTeam
Indicates that a given team is the one with the highest score (or total points) in a particular game, season, or competition.
-
E.
topScorerCompetition
Indicates that an entity is the highest-scoring participant in a specified competition.
- F. None of above. chosen
Provenance (4 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_69f2245de2f48190a481404896b56254 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f676fe75988190ae1bffb155a3c4e1 |
completed | May 2, 2026, 10:13 p.m. |
| PD | Predicate disambiguation | batch_69f66ec8298c8190b41fe9d182c05676 |
completed | May 2, 2026, 9:38 p.m. |
| PDg | Predicate description generation | batch_69f67256d064819094be04fc1bbbc635 |
completed | May 2, 2026, 9:53 p.m. |
Created at: April 29, 2026, 6:01 p.m.