Triple
T17069738
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Violet |
E414181
|
entity |
| Predicate | hasName |
P744
|
FINISHED |
| Object | Violet |
unclear NED1
|
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, hasName, Violet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Violet Context triple: [Violet, hasName, 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
Violet is the given first name of the English actress Anne Heywood, known for her film and television roles in the mid-20th century.
-
C.
Violet
Violet is a small, typically purple-flowered plant commonly found in temperate regions and widely recognized as a symbol of modesty and springtime.
-
D.
Violet
Violet is a character portrayed by Australian actress Robin McLeavy, likely known from her work in film or television.
-
E.
Violet
Violet is the given first name of the renowned American opera singer Leontyne Price.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
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_69d886cef44c8190ba56c44b4e863e64 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dbbfb1f08190807301ff6e573cf5 |
completed | April 18, 2026, 7:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01235278d48190bacc7881b9eb1ea1 |
completed | May 11, 2026, 12:31 a.m. |
Created at: April 10, 2026, 5:34 a.m.