Triple
T31396685
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Checkers speech |
E800881
|
entity |
| Predicate | accusationSubject |
P171820
|
FINISHED |
| Object | secret campaign fund |
—
|
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: secret campaign fund | Statement: [Checkers speech, accusationSubject, secret campaign fund]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: accusationSubject Context triple: [Checkers speech, accusationSubject, secret campaign fund]
-
A.
accusationTarget
Indicates that one entity is the object or recipient of an accusation made by another entity.
-
B.
allegedSubject
Indicates that the referenced entity is claimed or accused to be the subject responsible for a particular action, event, or state, without confirming the truth of that claim.
-
C.
accusationType
Indicates the specific category or nature of an accusation made by one party against another.
-
D.
accusationContext
Indicates the situational or conversational setting in which an accusation is made, such as its background, circumstances, or framing.
-
E.
accusationDescription
Indicates that a statement or explanation is being provided that details the nature, content, or specifics of an accusation made by one party against another.
- 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_69f224ea9998819086ae2e4f4f4091c8 |
completed | April 29, 2026, 3:34 p.m. |
| NER | Named-entity recognition | batch_69f6a5f71b2c8190aade8a83f465be0c |
completed | May 3, 2026, 1:33 a.m. |
| PD | Predicate disambiguation | batch_69f69fe66df08190958558d63ee623d9 |
completed | May 3, 2026, 1:07 a.m. |
| PDg | Predicate description generation | batch_69f6a5f656ec81909e02b0b873303adf |
completed | May 3, 2026, 1:33 a.m. |
Created at: April 29, 2026, 9:19 p.m.