Triple
T12491550
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lil Kesh |
E298577
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Shoki |
E998562
|
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: Shoki | Statement: [Lil Kesh, notableWork, Shoki]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shoki Context triple: [Lil Kesh, notableWork, Shoki]
-
A.
Shoki
chosen
"Shoki" is a popular Nigerian street-hop song by Lil Kesh that helped propel him to mainstream fame and popularized a viral dance of the same name.
-
B.
Tajōmaru
Tajōmaru is the notorious bandit whose conflicting testimonies drive the plot and themes of truth and perception in Ryūnosuke Akutagawa’s short story "In a Grove."
-
C.
Shinzei
Shinzei was a prominent Japanese Buddhist monk of the Heian period known for his influential role in the development and propagation of Shingon esoteric teachings.
-
D.
Shin-Koiwa
Shin-Koiwa is a residential and commercial neighborhood in Tokyo known for its busy railway station, local shopping streets, and traditional shitamachi atmosphere.
-
E.
Masaru
Masaru is a Japanese given name commonly used for males and borne by various notable figures in fields such as technology, sports, and entertainment.
- 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_69d6ada377208190a36011199a4d8558 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94de3076c81909640c982d520ca6b |
completed | April 10, 2026, 7:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f67c6849d88190b33c9f50632811a8 |
completed | May 2, 2026, 10:36 p.m. |
Created at: April 8, 2026, 9:56 p.m.