Triple
T8639793
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Violet Affleck |
E204616
|
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: [Violet Affleck, givenName, Violet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Violet Context triple: [Violet Affleck, givenName, Violet]
-
A.
Violet
Violet is a live-action short film recognized with the Academy Award for Best Live Action Short Film at the 54th Oscars.
-
B.
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.
-
C.
Violet
Violet is a character portrayed by Australian actress Robin McLeavy, likely known from her work in film or television.
-
D.
Violet
Violet is the given first name of the renowned American opera singer Leontyne Price.
-
E.
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.
- 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_69ca834ca1c88190a11ffb0200342fac |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc47650a14819094855aa8d062ebbc |
completed | March 31, 2026, 10:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cebc340528819085c80b69d6d32a34 |
completed | April 2, 2026, 6:57 p.m. |
Created at: March 30, 2026, 6:28 p.m.