Triple
T20805785
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States v. Ray Nagin |
E512148
|
entity |
| Predicate | typeOfCorruption |
P114736
|
FINISHED |
| Object | pay-to-play scheme |
—
|
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: pay-to-play scheme | Statement: [United States v. Ray Nagin, typeOfCorruption, pay-to-play scheme]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfCorruption Context triple: [United States v. Ray Nagin, typeOfCorruption, pay-to-play scheme]
-
A.
corrupts
Indicates that one entity causes another entity, system, or process to become morally, functionally, or structurally degraded or impaired.
-
B.
corruptingForceType
chosen
Indicates a type or category of influence that causes moral, ethical, or structural degradation in the affected entity.
-
C.
corruptedFormOf
Indicates that one form of an expression, word, or name is a distorted, altered, or degraded version derived from another, more original form.
-
D.
typeOfFaulting
Indicates the kind or classification of geological faulting that characterizes the relationship between rock units or structures.
-
E.
corruptionLevel
Indicates the degree or extent to which unethical, illegal, or dishonest practices are present or influential in a given context.
- 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_69e0b4cc69f481908e98751e697b9df4 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c2cf1cbc819092d92625dfb107d0 |
completed | April 21, 2026, 12:20 a.m. |
| PD | Predicate disambiguation | batch_69e5c99ca55481908e8d434fa901cfd6 |
completed | April 20, 2026, 6:37 a.m. |
Created at: April 16, 2026, 12:40 p.m.