Triple
T17825054
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gamera vs. Guiron |
E445092
|
entity |
| Predicate | featuresCharacter |
P626
|
FINISHED |
| Object | Akio |
—
|
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: Akio | Statement: [Gamera vs. Guiron, featuresCharacter, Akio]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Akio Context triple: [Gamera vs. Guiron, featuresCharacter, Akio]
-
A.
Akio
chosen
Akio is a Japanese given name commonly used for males and borne by various notable figures in business, arts, and entertainment.
-
B.
Tetsuya
Tetsuya is a masculine Japanese given name commonly used for men in Japan and among Japanese communities worldwide.
-
C.
Yoichi
Yoichi is a coastal town in Hokkaido, Japan, known for its whisky distilleries, fruit orchards, and scenic location along the Sea of Japan.
-
D.
Kunio
Kunio is a Japanese masculine given name borne by various notable figures in politics, academia, and the arts.
-
E.
Masahiro
Masahiro is a Japanese given name commonly used for males and borne by various notable figures in entertainment, sports, and other fields.
- 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_69d8b9f0de78819099395b14db75a8a6 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e48914226c819083edcc78e00b2d42 |
completed | April 19, 2026, 7:49 a.m. |
Created at: April 10, 2026, 10:15 a.m.