Triple
T22049219
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brazil vs Germany (2014 FIFA World Cup) |
E544838
|
entity |
| Predicate | awayTeamWorldCupTitlesAfterTournament |
P146389
|
FINISHED |
| Object | 4 |
—
|
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: 4 | Statement: [Brazil vs Germany (2014 FIFA World Cup), awayTeamWorldCupTitlesAfterTournament, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: awayTeamWorldCupTitlesAfterTournament Context triple: [Brazil vs Germany (2014 FIFA World Cup), awayTeamWorldCupTitlesAfterTournament, 4]
-
A.
numberOfWorldCupWins
Indicates how many times an entity has won the FIFA World Cup tournament.
-
B.
WorldCupVictories
Indicates the number of times an entity has won the FIFA World Cup tournament.
-
C.
worldCupWon
Indicates that the subject has won the FIFA World Cup tournament.
-
D.
WorldCupWins
Indicates the number of times an entity (typically a national team) has won the FIFA World Cup tournament.
-
E.
WorldCupOverallTitles
Indicates the total number of World Cup championship titles an entity has won across all tournaments.
- 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_69e11e32445c8190ab97089b48a130bb |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f1283191e48190b4ddc84138243327 |
completed | April 28, 2026, 9:35 p.m. |
| PD | Predicate disambiguation | batch_69e6f643ca74819083e8ab78e843f243 |
completed | April 21, 2026, 4 a.m. |
| PDg | Predicate description generation | batch_69e6fad4a540819096cdd5ea08527220 |
completed | April 21, 2026, 4:19 a.m. |
Created at: April 16, 2026, 8:26 p.m.