Triple
T24726922
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stormy Daniels–Donald Trump scandal |
E618178
|
entity |
| Predicate | stormyDanielsProfession |
P109746
|
FINISHED |
| Object | adult film actress |
—
|
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: adult film actress | Statement: [Stormy Daniels–Donald Trump scandal, stormyDanielsProfession, adult film actress]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: stormyDanielsProfession Context triple: [Stormy Daniels–Donald Trump scandal, stormyDanielsProfession, adult film actress]
-
A.
typeOfPublicFigure
Indicates the specific category or role of public figure that an entity is classified as (e.g., politician, celebrity, activist).
-
B.
typeOfWorkHeIsKnownFor
chosen
Indicates the category or kind of work for which a person is recognized or notable.
-
C.
starOccupation
Indicates that an entity is the primary or featured performer in a particular occupation, role, or professional capacity.
-
D.
developerOfWorkAppearingIn
Indicates that one entity is the creator or developer of a work in which another entity appears or is featured.
-
E.
winnerProfession
Indicates that the associated profession is the occupation or field of work of the winner in a given event or competition.
- 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_69f410fe3b848190ae296a29f742ee30 |
completed | May 1, 2026, 2:33 a.m. |
| PD | Predicate disambiguation | batch_69f40ee8ada8819089a7016b50308ff0 |
completed | May 1, 2026, 2:24 a.m. |
Created at: April 18, 2026, 3:59 a.m.