Triple
T13597543
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Special Counsel investigation into Russian interference in the 2016 United States elections |
E324859
|
entity |
| Predicate | guiltyPleaCount |
P110230
|
FINISHED |
| Object | 8 |
—
|
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: 8 | Statement: [Special Counsel investigation into Russian interference in the 2016 United States elections, guiltyPleaCount, 8]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: guiltyPleaCount Context triple: [Special Counsel investigation into Russian interference in the 2016 United States elections, guiltyPleaCount, 8]
-
A.
numberOfConvictions
Indicates the count of times an entity has been formally found guilty of an offense.
-
B.
numberOfArrests
Indicates the count of times an entity has been arrested.
-
C.
numberOfIndictedPersonsApproximate
Indicates an approximate count of persons who have been formally indicted in a given context or case.
-
D.
convictedOf
Indicates that a person or entity has been found guilty of committing a specified offense or crime through a formal legal process.
-
E.
numberOfPeopleAccused
Indicates the count of individuals who are formally alleged to have committed a particular act or offense.
- 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_69d80769eaf081909d82f44e484d6113 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb0590558819080ccc5874a650b1e |
completed | April 12, 2026, 2:46 p.m. |
| PD | Predicate disambiguation | batch_69dbae18eaf48190809e8b365856cde9 |
completed | April 12, 2026, 2:37 p.m. |
| PDg | Predicate description generation | batch_69dbaf9f3bdc8190838539aaef1f422b |
completed | April 12, 2026, 2:43 p.m. |
Created at: April 9, 2026, 9:49 p.m.