Triple
T24726904
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stormy Daniels–Donald Trump scandal |
E618178
|
entity |
| Predicate | paymentBeneficiary |
P14855
|
FINISHED |
| Object | Stormy Daniels |
—
|
NE NERFINISHED |
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: Stormy Daniels | Statement: [Stormy Daniels–Donald Trump scandal, paymentBeneficiary, Stormy Daniels]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: paymentBeneficiary Context triple: [Stormy Daniels–Donald Trump scandal, paymentBeneficiary, Stormy Daniels]
-
A.
beneficiaryInstitution
Indicates that an institution is the recipient or beneficiary of an action, service, or resource.
-
B.
beneficiaryType
Indicates the type or category of beneficiary that receives or is intended to receive the benefit or outcome of an action or resource.
-
C.
beneficiaryActionMethod
Indicates that an action benefiting a recipient is carried out using a particular method or means.
-
D.
beneficiaryCountry
Indicates that one country is the recipient or beneficiary of aid, resources, or advantages provided in a given context.
-
E.
individualRecipient
chosen
Indicates that a specific individual is the direct recipient or beneficiary of something (such as an item, message, or action).
- 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_69f453035f508190be83a3d521723acf |
completed | May 1, 2026, 7:15 a.m. |
| PD | Predicate disambiguation | batch_69f44d6ef33081908f5d36ba1ae5f473 |
completed | May 1, 2026, 6:51 a.m. |
Created at: April 18, 2026, 3:59 a.m.