Triple
T24726918
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stormy Daniels–Donald Trump scandal |
E618178
|
entity |
| Predicate | involvesCharge |
P69104
|
FINISHED |
| Object | falsifying business records |
—
|
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: falsifying business records | Statement: [Stormy Daniels–Donald Trump scandal, involvesCharge, falsifying business records]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: involvesCharge Context triple: [Stormy Daniels–Donald Trump scandal, involvesCharge, falsifying business records]
-
A.
containsCharge
chosen
Indicates that one entity includes, holds, or encompasses a specific charge associated with it.
-
B.
hasCharge
Indicates that an entity possesses a specific electrical or formal charge, such as positive, negative, or neutral.
-
C.
chargesWere
Indicates that specific accusations or legal charges existed or were formally brought against an entity at some point in time.
-
D.
chargesVia
Indicates that one entity charges or powers another entity using a specified medium, method, or interface.
-
E.
chargesFeeFor
Indicates that one entity requires payment of a fee from another entity in exchange for a specific service, product, or privilege.
- 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_69e2fab772608190b74163751047ff50 |
completed | April 18, 2026, 3:29 a.m. |
| NER | Named-entity recognition | batch_69f5ffc74fa481909b4fe24a9337f9eb |
completed | May 2, 2026, 1:44 p.m. |
| PD | Predicate disambiguation | batch_69f5f7f99dc08190afcfb3bc4dfbec1d |
completed | May 2, 2026, 1:11 p.m. |
Created at: April 18, 2026, 3:59 a.m.