Triple
T9326342
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cheek to Cheek ballroom dance |
E224394
|
entity |
| Predicate | costumeWornByGingerRogers |
P87536
|
FINISHED |
| Object | blue feathered gown |
—
|
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: blue feathered gown | Statement: [Cheek to Cheek ballroom dance, costumeWornByGingerRogers, blue feathered gown]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: costumeWornByGingerRogers Context triple: [Cheek to Cheek ballroom dance, costumeWornByGingerRogers, blue feathered gown]
-
A.
hasGingerRogersRole
Indicates that an entity is assigned or associated with a role specifically identified as the "Ginger Rogers" role in a given context or production.
-
B.
costume
Indicates that one entity is wearing, dressed in, or outfitted with the other entity as a costume.
-
C.
costumeType
Indicates the specific kind or category of costume associated with an entity.
-
D.
designedCostumesFor
Indicates that one entity created or planned the costumes used by another entity, typically for a performance, production, or event.
-
E.
costumeFeatures
Indicates that a costume possesses or includes specific features, attributes, or decorative elements.
- 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_69ca8427a0c08190b749831d5ea98f02 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd36f88e988190bb896a3d7c3c723c |
completed | April 1, 2026, 3:17 p.m. |
| PD | Predicate disambiguation | batch_69cc7a643924819097f01144734901cf |
completed | April 1, 2026, 1:52 a.m. |
| PDg | Predicate description generation | batch_69cc94b796788190816b71b1e9996288 |
completed | April 1, 2026, 3:44 a.m. |
Created at: March 30, 2026, 7:39 p.m.