Triple
T16206795
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Emperor Kōnin |
E393348
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Shirakabe |
E1201308
|
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: Shirakabe | Statement: [Emperor Kōnin, givenName, Shirakabe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shirakabe Context triple: [Emperor Kōnin, givenName, Shirakabe]
-
A.
Shirakabe
chosen
Shirakabe was the personal name of Emperor Kōnin, a Nara-period Japanese ruler who reigned in the late 8th century.
-
B.
Kamitsumaki
Kamitsumaki is the first volume of the ancient Japanese chronicle Kojiki, focusing on Shinto creation myths and the age of the gods.
-
C.
Higashiizu
Higashiizu is a coastal town in Shizuoka Prefecture, Japan, known for its hot springs, scenic Pacific shoreline, and views of the Izu Islands.
-
D.
Shizunai
Shizunai was a former town in Hokkaido, Japan, known for its horse-breeding traditions and later incorporated into the town of Shinhidaka.
-
E.
Kitasenju
Kitasenju is a major commercial and transportation hub in Adachi, Tokyo, known for its busy train station, shopping complexes, and urban downtown atmosphere.
- 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_69d87f1f5bd08190bd01cac0d5b9d2ef |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e227101a3c819095ef40e50bf66433 |
completed | April 17, 2026, 12:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0017a90be08190bd9fb64abd424e1e |
completed | May 10, 2026, 5:29 a.m. |
Created at: April 10, 2026, 5:03 a.m.