Triple
T25508778
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Penelope Devereux |
E639314
|
entity |
| Predicate | literaryCharacterModeledAs |
P162887
|
FINISHED |
| Object | Stella |
—
|
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: Stella | Statement: [Penelope Devereux, literaryCharacterModeledAs, Stella]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: literaryCharacterModeledAs Context triple: [Penelope Devereux, literaryCharacterModeledAs, Stella]
-
A.
fictionalCharacter
Indicates that one entity is a fictional character that appears within the narrative world of another entity (such as a work, series, or franchise).
-
B.
fictionalCharacterAssisted
Indicates that one fictional character provided help, support, or assistance to another fictional character.
-
C.
literaryRole
Indicates the specific narrative or functional role an entity holds within a literary work or text.
-
D.
fictionalCharacterFrom
Indicates that a fictional character originates from, or is created within, a particular work, universe, or source.
-
E.
storyCharacterizedAs
Indicates that a story is described, portrayed, or defined as having a particular quality, style, or attribute.
- F. None of above. chosen
Provenance (4 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_69e75dbd09308190b6b5f0afdc12ec6d |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f62e83045c8190a424a2e401a88e9e |
completed | May 2, 2026, 5:04 p.m. |
| PD | Predicate disambiguation | batch_69f62c1379f08190836c3e02b0c892df |
completed | May 2, 2026, 4:53 p.m. |
| PDg | Predicate description generation | batch_69f62d886828819080ec2f742b9449e3 |
completed | May 2, 2026, 4:59 p.m. |
Created at: April 21, 2026, 2:48 p.m.