Triple
T33701699
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miss Erikson |
E863470
|
entity |
| Predicate | typicalFunctionInScenes |
P206642
|
FINISHED |
| Object | office and household business |
—
|
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: office and household business | Statement: [Miss Erikson, typicalFunctionInScenes, office and household business]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalFunctionInScenes Context triple: [Miss Erikson, typicalFunctionInScenes, office and household business]
-
A.
typicalFunction
Indicates that something serves as the usual or characteristic function or role of an entity.
-
B.
typicalFunctionClass
Indicates that something belongs to the usual or characteristic functional category associated with it.
-
C.
scenes
Indicates that one entity is a scene or setting in which the other entity occurs, appears, or is depicted.
-
D.
performedInScene
Indicates that an action or event took place within a specific scene or setting.
-
E.
filmSceneType
Indicates the type or category of a scene within a film, such as its narrative function, style, or setting.
- 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_69f3498723a08190ac034339cc78eade |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_6a037e0953908190b2930b3c06a40129 |
completed | May 12, 2026, 7:22 p.m. |
| PD | Predicate disambiguation | batch_6a0379f6c3308190b954f7810214ceed |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037e07fe4481909ca21eae7a941ee7 |
completed | May 12, 2026, 7:22 p.m. |
Created at: May 1, 2026, 1:43 a.m.