Triple
T22534931
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fort Minor |
E557128
|
entity |
| Predicate | hasSong |
P20452
|
FINISHED |
| Object | Kenji |
—
|
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: Kenji | Statement: [Fort Minor, hasSong, Kenji]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kenji Context triple: [Fort Minor, hasSong, Kenji]
-
A.
Kenji
chosen
Kenji is a common Japanese masculine given name used by various notable individuals across fields such as music, literature, and sports.
-
B.
Nobu
Nobu is a common Japanese given name and name element that can appear in various masculine and unisex names.
-
C.
Koki
Koki is a fictional character associated with Raka, likely appearing alongside them in a shared narrative or story.
-
D.
Masataka
Masataka is a Japanese given name commonly used for males.
-
E.
Jiro
Jiro is a masculine Japanese given name commonly associated with notable figures in Japanese culture, including engineers, artists, and fictional characters.
- 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_69e11e57483c8190b0887c4f8ff26446 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15edad0248190b990ddffbc786e05 |
completed | April 29, 2026, 1:28 a.m. |
Created at: April 16, 2026, 8:51 p.m.