Triple
T28095408
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Portrait of a Lady (Cornelis de Vos) |
E710073
|
entity |
| Predicate | portrayalQuality |
P117701
|
FINISHED |
| Object | elegant |
—
|
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: elegant | Statement: [Portrait of a Lady (Cornelis de Vos), portrayalQuality, elegant]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portrayalQuality Context triple: [Portrait of a Lady (Cornelis de Vos), portrayalQuality, elegant]
-
A.
hasPortrayalQuality
chosen
Indicates that something possesses a particular characteristic or standard of quality in how it is portrayed or represented.
-
B.
portrayalFeature
Indicates that one entity serves as a characteristic, aspect, or attribute highlighted in the depiction or representation of another entity.
-
C.
portrayalRecognition
Indicates that one entity recognizes or identifies another entity as a portrayal or representation of a particular subject or character.
-
D.
portrayalReception
Indicates how a particular portrayal of someone or something is received, evaluated, or responded to by an audience or observers.
-
E.
portrayalLanguage
Indicates the language in which something is depicted, represented, or expressed.
- 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_69ef9b70fd108190a875953b2e50ca91 |
completed | April 27, 2026, 5:22 p.m. |
| NER | Named-entity recognition | batch_69f7aa699d68819081ed363931894ab3 |
completed | May 3, 2026, 8:04 p.m. |
| PD | Predicate disambiguation | batch_69f7a8cec6d48190bebfa884b2f938c0 |
completed | May 3, 2026, 7:58 p.m. |
Created at: April 27, 2026, 9:01 p.m.