Triple
T27775558
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South Korea national football team |
E699173
|
entity |
| Predicate | hasStrongWorldCupRecordAmong |
P43244
|
FINISHED |
| Object | Asian teams |
—
|
LITERAL 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: Asian teams | Statement: [South Korea national football team, hasStrongWorldCupRecordAmong, Asian teams]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStrongWorldCupRecordAmong Context triple: [South Korea national football team, hasStrongWorldCupRecordAmong, Asian teams]
-
A.
worldCupRecord
chosen
Indicates a relationship that specifies an entity’s performance statistics or achievements in FIFA World Cup competitions.
-
B.
hasWorldCupOverallTitleRecord
Indicates that one entity holds the record for the most overall World Cup titles in comparison to others.
-
C.
hasWorldCupParticipation
Indicates that an entity has taken part in at least one edition of the FIFA World Cup tournament.
-
D.
numberOfWorldCupWins
Indicates how many times an entity has won the FIFA World Cup tournament.
-
E.
playedInWorldCupFor
Indicates that an individual participated as a player in at least one FIFA World Cup tournament representing a specified national team.
- 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_69fce7671f108190bf3ebf54339068b5 |
completed | May 7, 2026, 7:26 p.m. |
| PD | Predicate disambiguation | batch_69fce5b5a84c81908ac1b5b9f08d48d0 |
completed | May 7, 2026, 7:19 p.m. |
Created at: April 27, 2026, 5:05 p.m.