Triple
T15440371
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Koyasan, Wakayama Prefecture |
E369882
|
entity |
| Predicate | hasNumberOfTemples |
P43478
|
FINISHED |
| Object | over 100 |
—
|
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: over 100 | Statement: [Koyasan, Wakayama Prefecture, hasNumberOfTemples, over 100]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfTemples Context triple: [Koyasan, Wakayama Prefecture, hasNumberOfTemples, over 100]
-
A.
hasTempleCount
chosen
Indicates that an entity is associated with a specified number of temples.
-
B.
hasTempleOf
Indicates that a location or entity possesses, contains, or is the site of a temple dedicated to a particular deity, figure, or purpose.
-
C.
pilgrimageTempleCount
Indicates the number of temples associated with or visited during a particular pilgrimage.
-
D.
hasNumberOfMosques
Indicates the quantity of mosques associated with a given entity.
-
E.
hasNotableTemple
Indicates that an entity is associated with a temple that is recognized as particularly important, famous, or significant.
- 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_69d85a19180081909925012fbf4e62a3 |
completed | April 10, 2026, 2:02 a.m. |
| NER | Named-entity recognition | batch_69e03eddf258819082679970b7d2b6af |
completed | April 16, 2026, 1:43 a.m. |
| PD | Predicate disambiguation | batch_69ded28276f481908c2038bb301e57cf |
completed | April 14, 2026, 11:49 p.m. |
Created at: April 10, 2026, 3:21 a.m.