Triple
T38211492
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cuddlepie |
E1010556
|
entity |
| Predicate | isBelovedCharacterIn |
P12208
|
FINISHED |
| Object | Australian children's literature |
—
|
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: Australian children's literature | Statement: [Cuddlepie, isBelovedCharacterIn, Australian children's literature]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isBelovedCharacterIn Context triple: [Cuddlepie, isBelovedCharacterIn, Australian children's literature]
-
A.
isBelovedOf
Indicates that one entity is deeply loved, cherished, or held in special affection by another entity.
-
B.
meetsFictionalCharacter
Indicates that one entity encounters or comes into contact with a fictional character.
-
C.
hasBelovedRole
Indicates that an entity holds a role or position that is deeply cherished or loved by another entity.
-
D.
hasMainCharacterFrom
Indicates that a work of fiction has a main character who originates from or belongs to a specified place, group, or source.
-
E.
characterIn
chosen
Indicates that an entity appears as a character within a specified work, story, or narrative.
- 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_69f76dcdc7708190a5f1751d53f40ffe |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69fcc42cbac48190b8d3e4c9ce140838 |
completed | May 7, 2026, 4:56 p.m. |
| PD | Predicate disambiguation | batch_69fcb0fc69c88190800453eb57a7e62c |
completed | May 7, 2026, 3:34 p.m. |
Created at: May 3, 2026, 4:30 p.m.