Triple
T21471350
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2015 FIFA corruption case |
E529738
|
entity |
| Predicate | mainTypeOfCorruption |
P114736
|
FINISHED |
| Object | bribes for media rights |
—
|
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: bribes for media rights | Statement: [2015 FIFA corruption case, mainTypeOfCorruption, bribes for media rights]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainTypeOfCorruption Context triple: [2015 FIFA corruption case, mainTypeOfCorruption, bribes for media rights]
-
A.
corruptionLevel
Indicates the degree or extent to which unethical, illegal, or dishonest practices are present or influential in a given context.
-
B.
corrupts
Indicates that one entity causes another entity, system, or process to become morally, functionally, or structurally degraded or impaired.
-
C.
corruptingForceType
chosen
Indicates a type or category of influence that causes moral, ethical, or structural degradation in the affected entity.
-
D.
criminalType
Indicates the specific category or classification of crime associated with a criminal act or offender.
-
E.
committedCrime
Indicates that an entity has carried out or been responsible for a criminal act or offense.
- 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_69e0c459acb481909bb6ee452a0045c7 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e9ea13adfc819093324ae6fe66c3fd |
completed | April 23, 2026, 9:44 a.m. |
| PD | Predicate disambiguation | batch_69e631ec1d048190b6da97da8222e413 |
completed | April 20, 2026, 2:02 p.m. |
Created at: April 16, 2026, 6:18 p.m.