Triple
T22962849
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brazil vs Germany 2002 FIFA World Cup Final |
E570950
|
entity |
| Predicate | GermanyWorldCupTitlesAfterMatch |
P150404
|
FINISHED |
| Object | 3 |
—
|
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: 3 | Statement: [Brazil vs Germany 2002 FIFA World Cup Final, GermanyWorldCupTitlesAfterMatch, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: GermanyWorldCupTitlesAfterMatch Context triple: [Brazil vs Germany 2002 FIFA World Cup Final, GermanyWorldCupTitlesAfterMatch, 3]
-
A.
WorldCupWins
Indicates the number of times an entity (typically a national team) has won the FIFA World Cup tournament.
-
B.
WorldCupVictories
Indicates the number of times an entity has won the FIFA World Cup tournament.
-
C.
numberOfWorldCupWins
Indicates how many times an entity has won the FIFA World Cup tournament.
-
D.
AustraliaWorldCupTitlesAfterMatch
Indicates the number of World Cup titles Australia has won after the completion of a specific match.
-
E.
worldCupWon
Indicates that the subject has won the FIFA World Cup tournament.
- 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_69e245b212a88190b5259caf51606084 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f181f594fc8190816418486b798198 |
completed | April 29, 2026, 3:58 a.m. |
| PD | Predicate disambiguation | batch_69ef3b9101f48190a06c69dff26c6441 |
completed | April 27, 2026, 10:33 a.m. |
| PDg | Predicate description generation | batch_69ef538a115081908982597f79355840 |
completed | April 27, 2026, 12:16 p.m. |
Created at: April 17, 2026, 3:47 p.m.