Triple
T16059495
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Buzan-ha |
E389568
|
entity |
| Predicate | veneration |
P8505
|
FINISHED |
| Object | Kannon |
E234489
|
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: Kannon | Statement: [Buzan-ha, veneration, Kannon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kannon Context triple: [Buzan-ha, veneration, Kannon]
-
A.
Kannon
chosen
Kannon is the Japanese name for the bodhisattva of compassion, derived from the Buddhist deity Avalokiteshvara and widely venerated in Japan.
-
B.
Ōsu Kannon
Ōsu Kannon is a famous Buddhist temple in Nagoya, Japan, known for its large wooden statue of Kannon and its surrounding shopping arcade.
-
C.
Takkaze
Takkaze is a river in northern Ethiopia that flows through deep gorges before joining the Atbarah River, ultimately contributing to the Nile basin.
-
D.
Gono
Gono is a Zimbabwean surname most notably borne by Gideon Gono, the former governor of the Reserve Bank of Zimbabwe.
-
E.
Kankinara
Kankinara is a suburban locality in West Bengal, India, known for its railway station on the Kolkata suburban network and its surrounding residential and industrial areas.
- 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_69d86dae698881908327ef2d67706cb9 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1837729e4819086e7429e0a76b0d7 |
completed | April 17, 2026, 12:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffdbe678fc8190b36737a9cd29691c |
completed | May 10, 2026, 1:14 a.m. |
Created at: April 10, 2026, 4:57 a.m.