Triple
T11753761
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Manuel Neuer |
E279471
|
entity |
| Predicate | FIFAClubWorldCupTitle |
P15093
|
FINISHED |
| Object | 2013 with Bayern Munich |
—
|
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: 2013 with Bayern Munich | Statement: [Manuel Neuer, FIFAClubWorldCupTitle, 2013 with Bayern Munich]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: FIFAClubWorldCupTitle Context triple: [Manuel Neuer, FIFAClubWorldCupTitle, 2013 with Bayern Munich]
-
A.
FIFAConfederationsCupTitleYear
Indicates the specific year in which an entity won a FIFA Confederations Cup title.
-
B.
worldClubChallengeTitles
Indicates the number of World Club Challenge titles an entity has won.
-
C.
bestClubWorldCupFinish
Indicates the highest finishing position a club has ever achieved in the FIFA Club World Cup.
-
D.
IntercontinentalCupTitles
Indicates the number of Intercontinental Cup championships an entity (typically a sports team or club) has won.
-
E.
ClubWorldCupTitles
chosen
Indicates that the subject has won one or more FIFA Club World Cup titles.
- 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_69d6ab01038c819080714901502c84fc |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a50b8a14819092a7397d73f0a8e3 |
completed | April 10, 2026, 7:21 a.m. |
| PD | Predicate disambiguation | batch_69d88a813cc48190a3dfdc60e8af80ae |
completed | April 10, 2026, 5:28 a.m. |
Created at: April 8, 2026, 9:41 p.m.