Triple
T27775538
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South Korea national football team |
E699173
|
entity |
| Predicate | asianCupRunnersUp |
P111647
|
FINISHED |
| Object | 1980 AFC Asian Cup |
—
|
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: 1980 AFC Asian Cup | Statement: [South Korea national football team, asianCupRunnersUp, 1980 AFC Asian Cup]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: asianCupRunnersUp Context triple: [South Korea national football team, asianCupRunnersUp, 1980 AFC Asian Cup]
-
A.
asianCupRunnersUpYear
chosen
Indicates the year in which an entity finished as runners-up in the AFC Asian Cup tournament.
-
B.
AFCChampionsLeagueRunnersUp
Indicates that an entity finished in second place (as the runners-up) in the AFC Champions League competition.
-
C.
afcU23AsianCupRunnersUp
Indicates that an under-23 national team finished as the runner-up (second place) in the AFC U-23 Asian Cup tournament.
-
D.
asiaCupWinner
Indicates that one entity is the champion or winning team of the Asia Cup tournament in a given edition or year.
-
E.
AFCRunnerUp
Indicates that an entity finished in second place (runner-up) in the AFC (American Football Conference) championship for a given season or event.
- F. None of above.
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_69ef6a4b5a9081909c9111396c2be3d2 |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69f643ed0b7481908cf25f3afec0a61d |
completed | May 2, 2026, 6:35 p.m. |
| PD | Predicate disambiguation | batch_69f641dc8ff48190ab575d855616580c |
completed | May 2, 2026, 6:26 p.m. |
Created at: April 27, 2026, 5:05 p.m.