Triple
T30002061
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saigoku Kannon Pilgrimage |
E762194
|
entity |
| Predicate | traditionalCount |
P99808
|
FINISHED |
| Object | 33 temples |
—
|
LITERAL 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: 33 temples | Statement: [Saigoku Kannon Pilgrimage, traditionalCount, 33 temples]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: traditionalCount Context triple: [Saigoku Kannon Pilgrimage, traditionalCount, 33 temples]
-
A.
alternativeCounting
chosen
Indicates that there exists another valid way of counting or enumerating the same set of items or events, distinct from the primary counting method.
-
B.
traditionalCode
Indicates that something adheres to or is characterized by long-established, customary, or historically rooted practices, methods, or norms.
-
C.
traditionalRule
Indicates that an entity follows, embodies, or is governed by a customary or long-established rule or norm.
-
D.
traditionalCatch
Indicates that an entity captures or obtains something using customary or long-established methods rather than modern or unconventional techniques.
-
E.
traditionalBase
Indicates that one entity serves as the customary or historically established foundation or basis for another.
- F. None of above.
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_69f2246a47ac81909cf5213053687ffc |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69fd974d75e08190af46b1d608769f3b |
completed | May 8, 2026, 7:57 a.m. |
| PD | Predicate disambiguation | batch_69fd94ff792c8190bedf4a639d3da809 |
completed | May 8, 2026, 7:47 a.m. |
Created at: April 29, 2026, 6:41 p.m.