Triple
T5367712
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Violeta |
E103169
|
entity |
| Predicate | originalTitle |
P65
|
FINISHED |
| Object | Violeta |
E103169
|
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: Violeta | Statement: [Violeta, originalTitle, Violeta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Violeta Context triple: [Violeta, originalTitle, Violeta]
-
A.
Violeta
chosen
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.
-
B.
Violet
Violet is a small, typically purple-flowered plant commonly found in temperate regions and widely recognized as a symbol of modesty and springtime.
-
C.
Violet
Violet is a character portrayed by Australian actress Robin McLeavy, likely known from her work in film or television.
-
D.
Rosa
Rosa is a genus of flowering plants known for its ornamental roses, prized worldwide for their beauty, fragrance, and cultural symbolism.
-
E.
Rosa
Rosa is the birth name of Linda Christian, a Mexican film actress known as the first "Bond girl" for her role in the 1954 television adaptation of Casino Royale.
- 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_69bd43daa3e4819090b59d127db70e57 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd86856f688190a34ab93619bae134 |
completed | March 20, 2026, 5:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf2928859881908ac238feca132d07 |
completed | March 21, 2026, 11:26 p.m. |
Created at: March 20, 2026, 2:02 p.m.