Triple
T15143496
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Juke Jam |
E361745
|
entity |
| Predicate | associatedWithArtist |
P5936
|
FINISHED |
| Object | Towkio |
E361505
|
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: Towkio | Statement: [Juke Jam, associatedWithArtist, Towkio]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Towkio Context triple: [Juke Jam, associatedWithArtist, Towkio]
-
A.
Towkio
chosen
Towkio is a Chicago-based rapper and member of the SaveMoney collective known for his eclectic, genre-blending hip-hop style.
-
B.
Toker
Toker is a variant form of the given name or surname Tucker, used as an alternative spelling or derivative.
-
C.
Ticha
Ticha is a retired Portuguese basketball point guard best known for her standout WNBA career, particularly with the Sacramento Monarchs.
-
D.
Toki
Toki is a city in Gifu Prefecture, Japan, known for its long-standing ceramics industry and production of Mino ware.
-
E.
TONA
TONA is Japan’s nearly risk-free overnight reference interest rate used as a key benchmark in financial markets, particularly as a replacement for LIBOR in yen-denominated contracts.
- 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_69d85a0759908190b8a051d2e2a1cbe6 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e005c5c4248190b57234e3ccf2831b |
completed | April 15, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fee5e4752481908e456279a1dfbff6 |
completed | May 9, 2026, 7:44 a.m. |
Created at: April 10, 2026, 3:07 a.m.