Triple
T29921852
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Isabella Vere |
E759963
|
entity |
| Predicate | existsInFictionalTime |
P60994
|
FINISHED |
| Object | early 18th century Scotland |
—
|
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: early 18th century Scotland | Statement: [Isabella Vere, existsInFictionalTime, early 18th century Scotland]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: existsInFictionalTime Context triple: [Isabella Vere, existsInFictionalTime, early 18th century Scotland]
-
A.
fictionalTime
chosen
Indicates that the associated time or temporal reference exists only within a fictional or imagined context, rather than in real-world chronology.
-
B.
endsInFictionalYear
Indicates that an event, story, or timeline concludes in a year that exists only within a fictional or imagined setting.
-
C.
isSetInFictionalUniverse
Indicates that a narrative work takes place within a specific fictional universe or setting.
-
D.
worksInFictionalContext
Indicates that an entity performs work or fulfills a role within a fictional or imagined setting rather than in real-world circumstances.
-
E.
hasRealityCounterpartInFiction
Indicates that a fictional element corresponds to or is based on a real-world counterpart within a work of fiction.
- 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_69f2246189fc8190996b63ee1f9a2374 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f6779474588190a1eb620e694cb7e4 |
completed | May 2, 2026, 10:15 p.m. |
| PD | Predicate disambiguation | batch_69f66ec8298c8190b41fe9d182c05676 |
completed | May 2, 2026, 9:38 p.m. |
Created at: April 29, 2026, 6:14 p.m.