Triple
T34822695
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Ballack |
E1003828
|
entity |
| Predicate | numberOfTimesAwardReceived_GermanFootballerOfTheYear |
P40870
|
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: [Michael Ballack, numberOfTimesAwardReceived_GermanFootballerOfTheYear, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfTimesAwardReceived_GermanFootballerOfTheYear Context triple: [Michael Ballack, numberOfTimesAwardReceived_GermanFootballerOfTheYear, 3]
-
A.
numberOfGermanFootballerOfTheYearAwards
chosen
Indicates the number of times an entity has received the German Footballer of the Year award.
-
B.
numberOfTimesNamedGermanRefereeOfTheYear
Indicates how many times an individual has been awarded the title "German Referee of the Year."
-
C.
wonBundesligaWith
Indicates that one entity achieved victory in the Bundesliga while being associated with (e.g., playing for or coaching) the other entity.
-
D.
numberOfGermanChampionships
Indicates the count of German championship titles associated with a given entity.
-
E.
timesWonBallonDor
Indicates the number of times an entity has received the Ballon d'Or award.
- 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_69f76db717088190811b4e744610f37d |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_6a037c8c34f88190ace26f555827f23e |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379ff1ba081908eda86acefcf69fb |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4 p.m.