Triple
T1082658
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pierrot |
E23980
|
entity |
| Predicate | typicalCostume |
P5541
|
FINISHED |
| Object | loose white clothing |
—
|
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: loose white clothing | Statement: [Pierrot, typicalCostume, loose white clothing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalCostume Context triple: [Pierrot, typicalCostume, loose white clothing]
-
A.
costumeType
chosen
Indicates the specific kind or category of costume associated with an entity.
-
B.
associatedCostumeInSummerStock
Indicates that one entity is linked to a costume used or worn by another entity in a summer stock (seasonal theater) production.
-
C.
originalCostumeColor
Indicates the color that a costume originally had before any changes, damage, or alterations.
-
D.
nationalDress
Indicates that an item of clothing is recognized as the traditional or customary dress associated with a particular nation or culture.
-
E.
haveDistinctCostume
Indicates that the entities each possess a costume that is different from the others’ costumes.
- 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_69a493f1ddf48190a99d54b00e99f8ce |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b95e56948190a1e92367ad7240b7 |
completed | March 1, 2026, 10:10 p.m. |
| PD | Predicate disambiguation | batch_69a4b73f4310819086281f8ec67d1a32 |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:42 p.m.