Triple
T27331437
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tregony |
E689809
|
entity |
| Predicate | electoralCorruption |
P51943
|
FINISHED |
| Object | notorious for patronage and bribery |
—
|
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: notorious for patronage and bribery | Statement: [Tregony, electoralCorruption, notorious for patronage and bribery]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: electoralCorruption Context triple: [Tregony, electoralCorruption, notorious for patronage and bribery]
-
A.
electoralCorruptionPerception
chosen
Indicates the extent to which elections are perceived as being influenced by corrupt practices such as bribery, fraud, or undue manipulation.
-
B.
electoralDignity
Indicates a relationship where an entity holds or is associated with an official electoral office, rank, or honor within a political or voting system.
-
C.
hasCorruptOfficials
Indicates that an entity possesses or is associated with officials who engage in corrupt or unethical behavior.
-
D.
controlledElections
Indicates that one party exerted undue influence or manipulation over an electoral process, compromising its fairness or independence.
-
E.
courtCorruption
Indicates that a court or judicial body is involved in corrupt practices, such as bribery, bias, or abuse of legal authority.
- 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_69ef355d4cb08190ab032c0a2e7d3753 |
completed | April 27, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69f7c29e1b848190b945c6c6120a5330 |
completed | May 3, 2026, 9:48 p.m. |
| PD | Predicate disambiguation | batch_69f7c1b6e7a881908deb96bedb2713f4 |
completed | May 3, 2026, 9:44 p.m. |
Created at: April 27, 2026, 11:38 a.m.