Triple
T12544975
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clinton impeachment |
E299937
|
entity |
| Predicate | senateVoteOnPerjuryArticle |
P25770
|
FINISHED |
| Object | 45 guilty – 55 not guilty |
—
|
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: 45 guilty – 55 not guilty | Statement: [Clinton impeachment, senateVoteOnPerjuryArticle, 45 guilty – 55 not guilty]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: senateVoteOnPerjuryArticle Context triple: [Clinton impeachment, senateVoteOnPerjuryArticle, 45 guilty – 55 not guilty]
-
A.
notableRepublicanVotingToConvictOnArticleI
Indicates that the subject is a prominent Republican who voted to convict on the first article (Article I) of an impeachment.
-
B.
senateVoteTallyFor
chosen
Indicates the recorded vote count or outcome in the senate for a particular measure, motion, or item.
-
C.
numberOfArticlesOfImpeachment
Indicates the specific count of formal impeachment charges brought against a person or officeholder.
-
D.
canImpeach
Indicates that one entity has the authority or power to formally impeach another entity.
-
E.
impeachmentConvictionThreshold
Indicates the required level of support or number of votes needed to convict an official in an impeachment proceeding.
- 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_69d6ada707008190aaec1238117c9379 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d95f5507b481908d13cc317b7402f6 |
completed | April 10, 2026, 8:36 p.m. |
| PD | Predicate disambiguation | batch_69d95410d0b0819097646edd1b837104 |
completed | April 10, 2026, 7:48 p.m. |
Created at: April 8, 2026, 9:57 p.m.