Triple
T26519187
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Susan Pevensie |
E669904
|
entity |
| Predicate | magicalObject |
P160599
|
FINISHED |
| Object | ivory horn given by Father Christmas |
—
|
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: ivory horn given by Father Christmas | Statement: [Susan Pevensie, magicalObject, ivory horn given by Father Christmas]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: magicalObject Context triple: [Susan Pevensie, magicalObject, ivory horn given by Father Christmas]
-
A.
magicalObjectThatControls
Indicates a relationship where one magical object has the power to direct, influence, or command another entity or force.
-
B.
typeOfMagic
Indicates that one entity is a specific category, school, or kind of magic associated with another entity.
-
C.
usesMagic
Indicates that an entity performs actions or achieves effects by employing magical powers or supernatural abilities.
-
D.
magicField
Indicates the presence or influence of a magical force or energy affecting an entity or area.
-
E.
usesMagicFor
Indicates that one entity employs or applies magic as a means to achieve, affect, or perform something involving another entity or context.
- 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_69eeb31b6dcc8190b30632dc3928a0c0 |
completed | April 27, 2026, 12:51 a.m. |
| NER | Named-entity recognition | batch_69f613c06530819095609b53dda121b4 |
completed | May 2, 2026, 3:09 p.m. |
| PD | Predicate disambiguation | batch_69f602d7b1b0819095ddd3b5169f8ce2 |
completed | May 2, 2026, 1:57 p.m. |
| PDg | Predicate description generation | batch_69f6037bf7a081908862a8359be80cf8 |
completed | May 2, 2026, 2 p.m. |
Created at: April 27, 2026, 1:26 a.m.