Triple
T14473870
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Portrait of a Woman (Rijksmuseum) |
E358915
|
entity |
| Predicate | depictsFashionOf |
P104989
|
FINISHED |
| Object | early 17th-century Dutch 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: early 17th-century Dutch clothing | Statement: [Portrait of a Woman (Rijksmuseum), depictsFashionOf, early 17th-century Dutch clothing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: depictsFashionOf Context triple: [Portrait of a Woman (Rijksmuseum), depictsFashionOf, early 17th-century Dutch clothing]
-
A.
depicts costume
chosen
Indicates that one entity visually represents or portrays the clothing or outfit associated with another entity.
-
B.
fashionStyle
Indicates the characteristic way in which an entity dresses or presents themselves in terms of clothing and appearance.
-
C.
fashionCharacteristic
Indicates a relationship where one entity possesses or exhibits a particular style, trend, or fashion-related attribute in relation to another.
-
D.
depictsPerson
Indicates that one entity visually represents or portrays a specific person.
-
E.
depicts
Indicates that one entity visually represents, portrays, or shows another entity.
- 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_69d827966698819082e140837737501d |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de91fab21c819090b6e209d8efba6e |
completed | April 14, 2026, 7:14 p.m. |
| PD | Predicate disambiguation | batch_69de5c42bd3c81909a62acf30cc24d1e |
completed | April 14, 2026, 3:24 p.m. |
Created at: April 10, 2026, 1:20 a.m.