Triple
T35371851
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sandra Bullock as Sarah Ashburn |
E1021792
|
entity |
| Predicate | worksOnCaseType |
P31978
|
FINISHED |
| Object | drug trafficking investigation |
—
|
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: drug trafficking investigation | Statement: [Sandra Bullock as Sarah Ashburn, worksOnCaseType, drug trafficking investigation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: worksOnCaseType Context triple: [Sandra Bullock as Sarah Ashburn, worksOnCaseType, drug trafficking investigation]
-
A.
caseTypes
Indicates the types or categories of cases associated with or applicable to an entity or situation.
-
B.
worksOnIssue
Indicates that an entity (typically a person or team) is actively engaged in addressing, resolving, or contributing work toward a specific issue.
-
C.
typeOfCasesHandled
chosen
Indicates the categories or kinds of cases that an entity (such as a person, organization, or system) is responsible for managing or processing.
-
D.
worksOnRegulationType
Indicates that an entity is involved in work or activities related to a specific type or category of regulation.
-
E.
worksOnDocumentType
Indicates that an entity is involved in handling, processing, or performing tasks related to a specific type or category of document.
- 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_69f76df000488190ab7c97f565677055 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69fcc4b700748190ae00b21d09c96695 |
completed | May 7, 2026, 4:58 p.m. |
| PD | Predicate disambiguation | batch_69fcb0f9d3d881908a049475182fb039 |
completed | May 7, 2026, 3:34 p.m. |
Created at: May 3, 2026, 4:03 p.m.