Triple
T30179272
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hortense Bellacourt |
E767154
|
entity |
| Predicate | setInFictionalEra |
P18945
|
FINISHED |
| Object | early 20th century American high society |
—
|
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 20th century American high society | Statement: [Hortense Bellacourt, setInFictionalEra, early 20th century American high society]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: setInFictionalEra Context triple: [Hortense Bellacourt, setInFictionalEra, early 20th century American high society]
-
A.
setInFictionalYear
Indicates that the events or narrative of a work are situated in a specified fictional or non-real calendar year.
-
B.
fictionalEra
chosen
Indicates the time period or age within a fictional or imaginary setting in which an entity exists or an event occurs.
-
C.
setInFictionalLocation
Indicates that an event, story, or narrative takes place within a fictional or imagined location rather than a real-world setting.
-
D.
setInFictionalizedRegionOf
Indicates that an event or narrative is located within a region that is a fictionalized or altered version of a real-world place.
-
E.
endsInFictionalYear
Indicates that an event, story, or timeline concludes in a year that exists only within a fictional or imagined setting.
- 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_69f2247ba20c81909d34f2bfed706e1e |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f67f402b9c8190b01ed0fc50b7f5e8 |
completed | May 2, 2026, 10:48 p.m. |
| PD | Predicate disambiguation | batch_69f673c7a4588190837854f3ef61e6bf |
completed | May 2, 2026, 9:59 p.m. |
Created at: April 29, 2026, 7:26 p.m.