Triple
T9801355
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Basin City |
E237843
|
entity |
| Predicate | corruptionLevel |
P90044
|
FINISHED |
| Object | high |
—
|
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: high | Statement: [Basin City, corruptionLevel, high]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: corruptionLevel Context triple: [Basin City, corruptionLevel, high]
-
A.
courtCorruptionLevel
Indicates the degree to which a court is affected by corrupt practices or improper influence in its decisions and operations.
-
B.
electoralCorruptionPerception
Indicates the extent to which elections are perceived as being influenced by corrupt practices such as bribery, fraud, or undue manipulation.
-
C.
courtCorruption
Indicates that a court or judicial body is involved in corrupt practices, such as bribery, bias, or abuse of legal authority.
-
D.
corrupts
Indicates that one entity causes another entity, system, or process to become morally, functionally, or structurally degraded or impaired.
-
E.
lawEnforcementLevel
Indicates the degree or intensity of law enforcement presence, activity, or strictness applied in a given context.
- F. None of above. chosen
Provenance (4 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_69ca84dd4608819097ff4ed00feca280 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cda62a11a88190880e0cce24923b14 |
completed | April 1, 2026, 11:11 p.m. |
| PD | Predicate disambiguation | batch_69cd03da45a88190b71b1be3354c15a6 |
completed | April 1, 2026, 11:39 a.m. |
| PDg | Predicate description generation | batch_69cd06abc9248190a506b64e9c516d03 |
completed | April 1, 2026, 11:51 a.m. |
Created at: March 30, 2026, 8:29 p.m.