Triple
T10387326
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DreamWorks Theatre Featuring Kung Fu Panda |
E244794
|
entity |
| Predicate | featuresPreShow |
P93916
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [DreamWorks Theatre Featuring Kung Fu Panda, featuresPreShow, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresPreShow Context triple: [DreamWorks Theatre Featuring Kung Fu Panda, featuresPreShow, yes]
-
A.
displaysFeature
Indicates that one entity presents, shows, or makes visible a particular feature or characteristic of another entity.
-
B.
featuresIn
Indicates that an entity appears or plays a role within another entity, such as a person or element being included in a work, event, or context.
-
C.
featuresDecor
Indicates that one entity includes or showcases the decor elements provided or defined by another entity.
-
D.
featureSet
Indicates that one entity is a collection or configuration of features associated with or applied to another entity.
-
E.
featuresSuit
Indicates that one entity includes or presents a particular suit (e.g., clothing, armor, or outfit) as a notable component or attribute.
- 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_69d381b5116081908d85227bab6d3c0c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e9a4e6748190bd9dd319de94c659 |
completed | April 7, 2026, 11:25 a.m. |
| PD | Predicate disambiguation | batch_69d4dfb0e7a88190bec0b7a52c70dfe2 |
completed | April 7, 2026, 10:42 a.m. |
| PDg | Predicate description generation | batch_69d4e91ce2008190af252c140370b7f2 |
completed | April 7, 2026, 11:23 a.m. |
Created at: April 6, 2026, 12:05 p.m.