Triple
T5411677
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dion Fortune |
E121025
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Violet |
E75564
|
NE 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: Violet | Statement: [Dion Fortune, givenName, Violet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Violet Context triple: [Dion Fortune, givenName, Violet]
-
A.
Violet
chosen
Violet is a small, typically purple-flowered plant commonly found in temperate regions and widely recognized as a symbol of modesty and springtime.
-
B.
Violet
Violet is a character portrayed by Australian actress Robin McLeavy, likely known from her work in film or television.
-
C.
Violeta
Violeta is a novel by Chilean author Isabel Allende that follows the tumultuous, century-long life of a woman born during the 1918 Spanish flu pandemic.
-
D.
Rosamorada
Rosamorada is a municipality and town in the Mexican state of Nayarit, known for its agricultural activities and rural communities.
-
E.
Black and Violet
"Black and Violet" is an abstract painting by Wassily Kandinsky that exemplifies his pioneering use of color and geometric forms to evoke emotional and spiritual responses.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69bd463a41cc8190b32ff5af2b96ca93 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd87b9e578819086380ff18a633cb0 |
completed | March 20, 2026, 5:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf33a22e188190861459f74c3f615a |
completed | March 22, 2026, 12:11 a.m. |
Created at: March 20, 2026, 2:05 p.m.