Triple
T16833461
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Winter Dreams |
E409204
|
entity |
| Predicate | protagonistLaterOccupation |
P104743
|
FINISHED |
| Object | successful businessman |
—
|
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: successful businessman | Statement: [Winter Dreams, protagonistLaterOccupation, successful businessman]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: protagonistLaterOccupation Context triple: [Winter Dreams, protagonistLaterOccupation, successful businessman]
-
A.
featuresProtagonistOccupation
Indicates that the work’s main character has a specified occupation or job role.
-
B.
laterOccupationInFiction
chosen
Indicates that a fictional character holds a particular occupation at a later point in the narrative or timeline, distinct from their earlier roles.
-
C.
characterFormerOccupation
Indicates that a character previously held a specific occupation but no longer does.
-
D.
protagonistSocialStatus
Indicates the social standing or class position held by the story’s main character in relation to others in their society.
-
E.
occupationInEpilogue
Indicates that an entity holds a particular occupation or role specifically in the epilogue portion of a narrative or story.
- 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_69d883952b048190887740a980b712ed |
completed | April 10, 2026, 4:59 a.m. |
| NER | Named-entity recognition | batch_69e3b31981ac8190bbd9720efe842778 |
completed | April 18, 2026, 4:36 p.m. |
| PD | Predicate disambiguation | batch_69e32b87b4248190aaddb05e88452356 |
completed | April 18, 2026, 6:58 a.m. |
Created at: April 10, 2026, 5:23 a.m.