Triple
T2613198
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | To-ji |
E58823
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object | Kobo Daishi |
E75515
|
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: Kobo Daishi | Statement: [To-ji, associatedWith, Kobo Daishi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kobo Daishi Context triple: [To-ji, associatedWith, Kobo Daishi]
-
A.
Kobo Daishi
chosen
Kobo Daishi, also known as Kukai, was a Japanese Buddhist monk, scholar, and founder of the Shingon school of esoteric Buddhism who became one of Japan’s most revered religious figures.
-
B.
Gyōki
Gyōki was an influential Japanese Buddhist monk of the Nara period known for his public works, social welfare activities, and role in promoting Buddhism among the common people.
-
C.
Sōri Daijin
Sōri Daijin is the Japanese term for the Prime Minister, the head of government and chief executive authority of Japan.
-
D.
Heiga Zen
Heiga Zen is a researcher in speech synthesis and machine learning, known for helping develop Google's WaveNet neural network for generating raw audio.
-
E.
Tatsuno Kingo
Tatsuno Kingo was a prominent Japanese architect of the Meiji era, best known for pioneering Western-style brick architecture in Japan and designing landmark buildings such as Tokyo Station.
- 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_69ab4ac444dc819099614e534dd6021f |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abd87dcfbc8190b264062002bfe4ba |
completed | March 7, 2026, 7:49 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af83e816808190aa2c91801fd3c6c7 |
completed | March 10, 2026, 2:37 a.m. |
Created at: March 6, 2026, 9:50 p.m.