Triple
T16268855
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yamabe |
E394943
|
entity |
| Predicate | eraName |
P2938
|
FINISHED |
| Object | Enryaku |
E393349
|
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: Enryaku | Statement: [Yamabe, eraName, Enryaku]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Enryaku Context triple: [Yamabe, eraName, Enryaku]
-
A.
Enryaku
chosen
Enryaku was a Japanese era during the reign of Emperor Kanmu, notable for major political reforms and the early development of Kyoto as the imperial capital.
-
B.
Yasakaji
Yasakaji is a Buddhist temple in Japan known as Temple 47 on the Shikoku Pilgrimage.
-
C.
Genryaku
Genryaku was a late 12th-century Japanese era notable for political upheaval and the rise of the Kamakura shogunate.
-
D.
Arinori
Arinori was a given name of Mori Arinori, a prominent Meiji-era Japanese statesman and reformer known for modernizing Japan’s education system.
-
E.
Seishirō
Seishirō is a Japanese given name commonly used for male individuals.
- 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_69d87f221d8081909b0b2063e7528ba2 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e245ca5c708190a1e98ab37740c032 |
completed | April 17, 2026, 2:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0017bceb3881909dbd3167820ef199 |
completed | May 10, 2026, 5:29 a.m. |
Created at: April 10, 2026, 5:05 a.m.