Triple
T12928863
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tiffany Mitchell |
E309316
|
entity |
| Predicate | storylineFeature |
P35676
|
FINISHED |
| Object | marital problems with Grant Mitchell |
—
|
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: marital problems with Grant Mitchell | Statement: [Tiffany Mitchell, storylineFeature, marital problems with Grant Mitchell]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: storylineFeature Context triple: [Tiffany Mitchell, storylineFeature, marital problems with Grant Mitchell]
-
A.
storyline
Indicates that one entity serves as the narrative plot or sequence of events associated with another entity.
-
B.
narrativeFeature
Indicates that one element functions as a narrative-related characteristic, device, or structural component of another.
-
C.
storyElement
chosen
Indicates that one entity functions as a narrative component or part within the structure of another entity’s story.
-
D.
storyFunction
Indicates that one entity serves a particular narrative role or function within the story structure of another entity.
-
E.
storyBy
Indicates that one entity is the creator or author of the story associated with another entity.
- 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_69d7bdfa933c8190b5a27aa4a08a19b7 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d971ec72a48190aceef10630603d2c |
completed | April 10, 2026, 9:55 p.m. |
| PD | Predicate disambiguation | batch_69d96fab4d0881909a7a4d66bab9aa85 |
completed | April 10, 2026, 9:46 p.m. |
Created at: April 9, 2026, 5:42 p.m.