Triple
T38117434
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bewitched Bunny |
E951828
|
entity |
| Predicate | featuresFairyTaleElement |
P115956
|
FINISHED |
| Object | wicked witch |
—
|
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: wicked witch | Statement: [Bewitched Bunny, featuresFairyTaleElement, wicked witch]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresFairyTaleElement Context triple: [Bewitched Bunny, featuresFairyTaleElement, wicked witch]
-
A.
featuresFictionalElement
chosen
Indicates that one entity includes, presents, or incorporates a fictional element (such as an imaginary character, place, object, or concept) as part of its content or composition.
-
B.
featuresFictionalForm
Indicates that one entity includes, presents, or incorporates a fictional representation or version of another entity.
-
C.
featuresCharacterWith
Indicates that one entity (such as a work or product) includes or presents a particular character as part of its content.
-
D.
featuresMythicalObject
Indicates that something includes, presents, or prominently involves a mythical or legendary object.
-
E.
featuresPrincess
Indicates that something includes or prominently presents a princess as a central element or 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_69f76f07734c8190814e937e12257a78 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a037df1223c8190a5d61e4f8e6fd613 |
completed | May 12, 2026, 7:22 p.m. |
| PD | Predicate disambiguation | batch_6a037a1ad6c48190bfe35d350c1b4751 |
completed | May 12, 2026, 7:06 p.m. |
Created at: May 3, 2026, 4:21 p.m.