Triple
T5495001
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Argentina national football team |
E144188
|
entity |
| Predicate | FIFAConfederationsCupTitleYear |
P65063
|
FINISHED |
| Object | 1992 |
—
|
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: 1992 | Statement: [Argentina national football team, FIFAConfederationsCupTitleYear, 1992]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: FIFAConfederationsCupTitleYear Context triple: [Argentina national football team, FIFAConfederationsCupTitleYear, 1992]
-
A.
wonConfederationsCup
Indicates that the subject has won the FIFA Confederations Cup tournament.
-
B.
ConfederationsCupTitles
Indicates the number of FIFA Confederations Cup championships an entity has won.
-
C.
WorldCupOverallTitles
Indicates the total number of World Cup championship titles an entity has won across all tournaments.
-
D.
worldCupTitle
Indicates that an entity has won a FIFA World Cup championship title.
-
E.
WorldCupSeasonTitles
Indicates the number of World Cup titles an entity has won in a given season or across seasons.
- 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_69c008f5a2748190bce7a39aabf87a6d |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01f08c2a4819093e772a1497c7ecc |
completed | March 22, 2026, 4:55 p.m. |
| PD | Predicate disambiguation | batch_69c01b052f3c81909f71c6add0f35a6f |
completed | March 22, 2026, 4:38 p.m. |
| PDg | Predicate description generation | batch_69c01f051e508190b3886d87b4afdd0b |
completed | March 22, 2026, 4:55 p.m. |
Created at: March 22, 2026, 3:31 p.m.