Triple
T24017205
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nakayama-dera |
E594712
|
entity |
| Predicate | pilgrimageNumberingSystem |
P113873
|
FINISHED |
| Object | Saigoku 33 Kannon temples |
—
|
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: Saigoku 33 Kannon temples | Statement: [Nakayama-dera, pilgrimageNumberingSystem, Saigoku 33 Kannon temples]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: pilgrimageNumberingSystem Context triple: [Nakayama-dera, pilgrimageNumberingSystem, Saigoku 33 Kannon temples]
-
A.
pilgrimageNumber
Indicates the number or count associated with a particular pilgrimage event or journey.
-
B.
hasPilgrimageNumber
Indicates that an entity is associated with a specific pilgrimage identification number, typically used to track or distinguish individual pilgrimages or pilgrims.
-
C.
pilgrimageScale
Indicates the extent or magnitude of a pilgrimage-related activity, such as its size, intensity, or level of significance.
-
D.
pilgrimageStopNumber
chosen
Indicates the ordinal position of a specific stop or location within a defined pilgrimage route or sequence.
-
E.
pilgrimageSince
Indicates that an entity has been engaged in or committed to a pilgrimage starting from a specified point in time.
- 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_69e288bc8f608190ac4af29f0bd1c744 |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1d5a6123c8190871e10cb81dfa819 |
completed | April 29, 2026, 9:55 a.m. |
| PD | Predicate disambiguation | batch_69f17639d23c8190bed93434e2f9230a |
completed | April 29, 2026, 3:08 a.m. |
Created at: April 17, 2026, 9:42 p.m.