Triple
T29975975
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dovima with Elephants |
E761446
|
entity |
| Predicate | featuresClothingDesigner |
P200861
|
FINISHED |
| Object | Christian Dior |
—
|
NE NERFINISHED |
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: Christian Dior | Statement: [Dovima with Elephants, featuresClothingDesigner, Christian Dior]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresClothingDesigner Context triple: [Dovima with Elephants, featuresClothingDesigner, Christian Dior]
-
A.
dressFeature
Indicates that a dress possesses or is characterized by a particular feature, attribute, or design element.
-
B.
designsClothesFor
Indicates that one entity creates or plans clothing specifically intended for another entity.
-
C.
clothingFeature
Indicates that one entity has a specific clothing-related attribute, detail, or characteristic associated with it.
-
D.
designsFor
Indicates that one entity creates or plans something specifically intended to serve, suit, or be used by another entity.
-
E.
fashionCharacteristic
Indicates a relationship where one entity possesses or exhibits a particular style, trend, or fashion-related attribute in relation to another.
- 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_69f22467626081908d5afea489590e96 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69ffb5c373948190a6606e8caa87a384 |
completed | May 9, 2026, 10:31 p.m. |
| PD | Predicate disambiguation | batch_69ffb261da788190b41399df8ed895e8 |
completed | May 9, 2026, 10:17 p.m. |
| PDg | Predicate description generation | batch_69ffb5c2cef0819087a92a1a5ab694a8 |
completed | May 9, 2026, 10:31 p.m. |
Created at: April 29, 2026, 6:33 p.m.