Triple
T16206833
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Enryaku |
E393349
|
entity |
| Predicate | precedes |
P97
|
FINISHED |
| Object | Daidō |
—
|
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: Daidō | Statement: [Enryaku, precedes, Daidō]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daidō Context triple: [Enryaku, precedes, Daidō]
-
A.
Daidō
chosen
Daidō was a Japanese era name (nengō) from the early Heian period, used during the reign of Emperor Kanmu.
-
B.
Fujinomiya
Fujinomiya is a city in Shizuoka Prefecture, Japan, known as a major gateway to Mount Fuji and for its scenic views of the iconic volcano.
-
C.
Ichigaya
Ichigaya is a central Tokyo district known for its major railway station, government and educational institutions, and proximity to the Imperial Palace area.
-
D.
Daikokucho
Daikokucho is a central urban neighborhood in Osaka known for its convenient transport links and proximity to major commercial and entertainment areas.
-
E.
Ryōtsu
Ryōtsu was a former city on Sado Island in Niigata Prefecture, Japan, known for its coastal setting and later incorporation into the city of Sado.
- 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_69d87f1f5bd08190bd01cac0d5b9d2ef |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e227101a3c819095ef40e50bf66433 |
completed | April 17, 2026, 12:26 p.m. |
Created at: April 10, 2026, 5:03 a.m.