Triple
T9457912
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Belle au bois dormant |
E228063
|
entity |
| Predicate | hasFairyTaleMotif |
P54524
|
FINISHED |
| Object | cursed princess |
—
|
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: cursed princess | Statement: [La Belle au bois dormant, hasFairyTaleMotif, cursed princess]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFairyTaleMotif Context triple: [La Belle au bois dormant, hasFairyTaleMotif, cursed princess]
-
A.
hasFairyCharacters
Indicates that the subject includes or features characters that are fairies.
-
B.
narrativeMotif
chosen
Indicates a recurring thematic element, pattern, or situation that appears across one or more narratives and helps structure or convey their underlying meanings.
-
C.
hasMythologicalFeature
Indicates that an entity possesses a characteristic, attribute, or element derived from mythology or mythological beings.
-
D.
hasFictionalForm
Indicates that an entity has a counterpart or representation that exists within a fictional or imaginary context.
-
E.
hasMythicMotif
Indicates that one entity features, embodies, or is associated with a particular mythic motif found in the other entity.
- 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_69ca843b123881909b0e60028475d12d |
completed | March 30, 2026, 2:10 p.m. |
| NER | Named-entity recognition | batch_69cd7f924f3081909dd937cbe6802881 |
completed | April 1, 2026, 8:26 p.m. |
| PD | Predicate disambiguation | batch_69cca55caaa8819089c5138e014892d3 |
completed | April 1, 2026, 4:55 a.m. |
Created at: March 30, 2026, 7:52 p.m.