Triple
T31445766
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cinderella’s Royal Table |
E802182
|
entity |
| Predicate | featuresCharacterAppearances |
P172090
|
FINISHED |
| Object | Cinderella |
—
|
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: Cinderella | Statement: [Cinderella’s Royal Table, featuresCharacterAppearances, Cinderella]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresCharacterAppearances Context triple: [Cinderella’s Royal Table, featuresCharacterAppearances, Cinderella]
-
A.
featuresCharactersFrom
Indicates that one entity (such as a work or production) includes or presents characters originating from another entity.
-
B.
characterAppearsIn
chosen
Indicates that a specific character is present or featured within a particular work, such as a book, movie, or episode.
-
C.
appearsAgainst
Indicates that one entity is visually or publicly presented in opposition to, or in contrast with, another entity.
-
D.
appearsWithCharacter
Indicates that two characters are shown or present together within the same scene, shot, or context.
-
E.
featuresCharacterDebut
Indicates that the subject work includes the first-ever appearance of a particular character.
- 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_69f348c5a6bc819092a557e95438976f |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_6a0079e152648190a9da2add94fc1831 |
completed | May 10, 2026, 12:28 p.m. |
| PD | Predicate disambiguation | batch_6a0078f77f9c8190af357a6016a2bd53 |
completed | May 10, 2026, 12:24 p.m. |
Created at: April 30, 2026, 9:09 p.m.