Triple
T3877519
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Satō |
E92538
|
entity |
| Predicate | hasVariantSpelling |
P457
|
FINISHED |
| Object | Satoh |
E92538
|
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: Satoh | Statement: [Satō, hasVariantSpelling, Satoh]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Satoh Context triple: [Satō, hasVariantSpelling, Satoh]
-
A.
Satō
chosen
Satō is a common Japanese surname borne by numerous notable figures in politics, arts, sports, and other fields.
-
B.
Saito
Saito is a Japanese surname commonly borne by notable figures in fields such as politics, sports, and the arts.
-
C.
Takaishi
Takaishi is a city in Osaka Prefecture, Japan, known as a small industrial and residential hub within the Osaka metropolitan area.
-
D.
Tanaka
Tanaka is a common Japanese surname borne by numerous notable figures in politics, arts, sports, and other fields.
-
E.
Wakatsuki
Wakatsuki was a Japanese destroyer of the Imperial Japanese Navy that served in World War II before being sunk during late-war Pacific naval operations.
- 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_69aed967448c819086c4b358d37b25aa |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeec72fa7c81909c73b3cf90597e9a |
completed | March 9, 2026, 3:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bec330d09c819085930d71b21acb7c |
completed | March 21, 2026, 4:11 p.m. |
Created at: March 9, 2026, 3:20 p.m.