Triple
T20206201
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sachiko |
E493357
|
entity |
| Predicate | hasRomanization |
P2508
|
FINISHED |
| Object | Sachiko |
—
|
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: Sachiko | Statement: [Sachiko, hasRomanization, Sachiko]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sachiko Context triple: [Sachiko, hasRomanization, Sachiko]
-
A.
Sachiko
chosen
Sachiko is a Japanese feminine given name that can be written with various kanji combinations, often conveying meanings related to happiness or child.
-
B.
Yoshiko
Yoshiko is a feminine Japanese given name commonly used across various generations and often associated with traditional Japanese culture.
-
C.
Shigeko
Shigeko is a Japanese feminine given name that has been borne by various notable women, including members of the imperial family.
-
D.
Kazuko
Kazuko is a Japanese feminine given name commonly borne by women, including members of the imperial family.
-
E.
Junko
Junko is a common Japanese feminine given name borne by numerous notable figures in fields such as entertainment, sports, and the arts.
- 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_69da6269614c8190bb40475d9d477358 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66d922ebc8190ae012da8ceba74dd |
completed | April 20, 2026, 6:16 p.m. |
Created at: April 11, 2026, 11:38 p.m.