Triple
T12398503
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Portrait of Countess Yulia Samoilova |
E296184
|
entity |
| Predicate | hasPortrayedRole |
P104626
|
FINISHED |
| Object | social status of the sitter |
—
|
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: social status of the sitter | Statement: [Portrait of Countess Yulia Samoilova, hasPortrayedRole, social status of the sitter]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPortrayedRole Context triple: [Portrait of Countess Yulia Samoilova, hasPortrayedRole, social status of the sitter]
-
A.
portrayedByAlsoPlays
Indicates that the actor who portrays a given character also plays another specified role or character.
-
B.
hasFictionalRole
Indicates that an entity plays or is assigned a specific role within a fictional work or narrative.
-
C.
hasPlayedRole
Indicates that an entity has performed or portrayed a particular role or character in some context (such as a film, play, or production).
-
D.
playedRoleIn
Indicates that an entity performed or assumed a specific role or character within a particular event, production, or context.
-
E.
portrayedBy
Indicates that one entity serves as the actor or performer who represents or plays the role of another entity in a work or medium.
- 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_69d6ad9f464c81909db36d7e96e34b9e |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d93fd448f08190af425a569d7ed158 |
completed | April 10, 2026, 6:22 p.m. |
| PD | Predicate disambiguation | batch_69d93ed4cea08190ad374d3f6a798053 |
completed | April 10, 2026, 6:17 p.m. |
| PDg | Predicate description generation | batch_69d93f607a88819089e89fd263ae9937 |
completed | April 10, 2026, 6:20 p.m. |
Created at: April 8, 2026, 9:54 p.m.