Triple
T18351003
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Haruki Murakami |
E439666
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Haruki |
—
|
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: Haruki | Statement: [Haruki Murakami, name, Haruki]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Haruki Context triple: [Haruki Murakami, name, Haruki]
-
A.
Haruki
chosen
Haruki is the given name of the acclaimed Japanese novelist Haruki Murakami, known for his surreal, introspective fiction.
-
B.
Kawakami
Kawakami is a Japanese surname borne by various notable individuals across fields such as sports, arts, and entertainment.
-
C.
Yoshimoto
Yoshimoto is a Japanese given name historically borne by notable samurai and daimyō, including the Sengoku-period warlord Imagawa Yoshimoto.
-
D.
Ryūnosuke
Ryūnosuke is a Japanese masculine given name most famously borne by the writer Ryūnosuke Akutagawa, often associated with literary and artistic circles.
-
E.
Kawabe Masakazu
Kawabe Masakazu was a Japanese general in the Imperial Japanese Army during World War II, known for leading major operations in the Burma campaign against Allied forces.
- 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_69d8b918221c8190a9f7b563d64ac677 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e514f83b648190b473cf611851c666 |
completed | April 19, 2026, 5:46 p.m. |
Created at: April 10, 2026, 10:37 a.m.