Triple
T20071562
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Captain McCluskey |
E499750
|
entity |
| Predicate | corruptionType |
P114736
|
FINISHED |
| Object | on the take from organized crime |
—
|
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: on the take from organized crime | Statement: [Captain McCluskey, corruptionType, on the take from organized crime]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: corruptionType Context triple: [Captain McCluskey, corruptionType, on the take from organized crime]
-
A.
corruptionLevel
Indicates the degree or extent to which unethical, illegal, or dishonest practices are present or influential in a given context.
-
B.
courtCorruption
Indicates that a court or judicial body is involved in corrupt practices, such as bribery, bias, or abuse of legal authority.
-
C.
courtCorruptionLevel
Indicates the degree to which a court is affected by corrupt practices or improper influence in its decisions and operations.
-
D.
hasCorruptOfficials
Indicates that an entity possesses or is associated with officials who engage in corrupt or unethical behavior.
-
E.
corruptingForceType
chosen
Indicates a type or category of influence that causes moral, ethical, or structural degradation in the affected entity.
- 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_69da627770948190997f486f9a2e370f |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66438633481908710907c48806499 |
completed | April 20, 2026, 5:36 p.m. |
| PD | Predicate disambiguation | batch_69e54cee7a5c819084ae4ff26419833f |
completed | April 19, 2026, 9:45 p.m. |
Created at: April 11, 2026, 3:40 p.m.