Triple
T38648457
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Masochism: Coldness and Cruelty and Venus in Furs |
E938778
|
entity |
| Predicate | settingOfIncludedNovel |
P182552
|
FINISHED |
| Object | Europe |
—
|
NE NERFINISHED |
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: Europe | Statement: [Masochism: Coldness and Cruelty and Venus in Furs, settingOfIncludedNovel, Europe]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: settingOfIncludedNovel Context triple: [Masochism: Coldness and Cruelty and Venus in Furs, settingOfIncludedNovel, Europe]
-
A.
hasNovella
Indicates that one entity possesses, includes, or is associated with a novella as part of its contents or attributes.
-
B.
bookSetting
chosen
Indicates the location, time period, or environment in which the events of a book take place.
-
C.
basedInNovel
Indicates that something (such as a work, adaptation, or element) is derived from, set in, or primarily grounded in the narrative world of a particular novel.
-
D.
laterSettingOfFiction
Indicates that one fictional work is set chronologically later than another within a shared narrative or story world.
-
E.
textsIncludedIn
Indicates that certain texts are contained within, or form a subset of, a larger collection or body of texts.
- 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_69f76ed948ec81908ce7811608a8f359 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fdd5fba5048190b7d430ae2054a1fd |
completed | May 8, 2026, 12:24 p.m. |
| PD | Predicate disambiguation | batch_69fdd35f76f88190a1854ea27132f9c7 |
completed | May 8, 2026, 12:13 p.m. |
Created at: May 3, 2026, 4:32 p.m.