Triple
T34194255
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Under Suspicion (2000 film) |
E877193
|
entity |
| Predicate | monicaBellucciRole |
P178508
|
FINISHED |
| Object | Chantal Hearst, the protagonist's much younger wife |
—
|
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: Chantal Hearst, the protagonist's much younger wife | Statement: [Under Suspicion (2000 film), monicaBellucciRole, Chantal Hearst, the protagonist's much younger wife]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: monicaBellucciRole Context triple: [Under Suspicion (2000 film), monicaBellucciRole, Chantal Hearst, the protagonist's much younger wife]
-
A.
MarilynMonroeRole
Indicates that the subject entity has (or had) a role or character portrayed by Marilyn Monroe.
-
B.
MarilynMonroeRoleType
Indicates the type or category of role associated with Marilyn Monroe in a given context.
-
C.
fiancéePortrayedBy
Indicates that a character’s fiancée is depicted or played by a specific actor or performer.
-
D.
leadActressCharacterName
Indicates the name of the character portrayed by the lead actress in a given work.
-
E.
targetAudienceOfWorkSheAppearsIn
Indicates the group of people for whom the work in which she appears is primarily intended or addressed.
- 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_69f349af20a4819089ac24d28f2d8112 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f710aaff588190adc6cc5b7d5424cc |
completed | May 3, 2026, 9:08 a.m. |
| PD | Predicate disambiguation | batch_69f70f3c5bfc81908585f52e196dafe5 |
completed | May 3, 2026, 9:02 a.m. |
| PDg | Predicate description generation | batch_69f70fddd43c819088dee5a448c72cbe |
completed | May 3, 2026, 9:05 a.m. |
Created at: May 1, 2026, 1:55 a.m.