Triple
T22210633
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Legally Blonde 2: Red, White & Blonde |
E548933
|
entity |
| Predicate | costumeColorMotif |
P66037
|
FINISHED |
| Object | pink |
—
|
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: pink | Statement: [Legally Blonde 2: Red, White & Blonde, costumeColorMotif, pink]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: costumeColorMotif Context triple: [Legally Blonde 2: Red, White & Blonde, costumeColorMotif, pink]
-
A.
colorAssociatedWithCostume
chosen
Indicates that a particular color is thematically or typically linked to a specific costume.
-
B.
costume
Indicates that one entity is wearing, dressed in, or outfitted with the other entity as a costume.
-
C.
costumeFeatures
Indicates that a costume possesses or includes specific features, attributes, or decorative elements.
-
D.
costumeType
Indicates the specific kind or category of costume associated with an entity.
-
E.
costumeDesignEmphasisOn
Indicates that a costume design places particular focus or priority on a specified element, style, feature, or thematic aspect.
- 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_69e11e3f7e04819089806d81d5ac431e |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12b2bcf748190a9721f0c9ae17e70 |
completed | April 28, 2026, 9:48 p.m. |
| PD | Predicate disambiguation | batch_69e71b4dcc408190a30429fb08fcf39e |
completed | April 21, 2026, 6:38 a.m. |
Created at: April 16, 2026, 8:36 p.m.