Triple
T17456278
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bittersweet World |
E425035
|
entity |
| Predicate | hasProducer |
P30366
|
FINISHED |
| Object | Kenna |
—
|
NE NERFINISHED |
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: Kenna | Statement: [Bittersweet World, hasProducer, Kenna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kenna Context triple: [Bittersweet World, hasProducer, Kenna]
-
A.
Kenna
chosen
Kenna is an Ethiopian-American singer-songwriter and producer known for his genre-blending alternative rock and electronic music, as well as collaborations with prominent artists and producers.
-
B.
Kaela
Kaela is the given name of American professional basketball player Kaela Davis.
-
C.
Keila
Keila is a small town in northern Estonia known for its historic church, scenic Keila River and waterfall, and role as a local administrative and transport hub.
-
D.
Kayl
Kayl is a commune in southwestern Luxembourg known for its industrial heritage and proximity to the country’s steel-producing region.
-
E.
Kaven
Kaven is one of the islands that make up Maloelap Atoll in the Marshall Islands, a Pacific island nation.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d889db0ba481908402409af3b37917 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e45141e1d48190b7de9159f1fd71fa |
completed | April 19, 2026, 3:51 a.m. |
Created at: April 10, 2026, 5:47 a.m.