Triple
T34608024
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oishi Park flower fields |
E888646
|
entity |
| Predicate | hasPhotospot |
P49165
|
FINISHED |
| Object | Mount Fuji with flower foreground |
—
|
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: Mount Fuji with flower foreground | Statement: [Oishi Park flower fields, hasPhotospot, Mount Fuji with flower foreground]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPhotospot Context triple: [Oishi Park flower fields, hasPhotospot, Mount Fuji with flower foreground]
-
A.
hasPhotoSpot
chosen
Indicates that a location or entity includes or is associated with a designated place suitable for taking photographs.
-
B.
hasPhotogenicFeature
Indicates that an entity possesses a visual characteristic or attribute that is especially attractive or appealing when photographed.
-
C.
hasPhotographAt
Indicates that a photograph depicting an entity was taken or exists at a specific location or event.
-
D.
isTouristPhotoSpot
Indicates that a location is recognized or designated as a popular place for tourists to take photos.
-
E.
hasPhotograph
Indicates that one entity possesses, includes, or is associated with a photograph depicting or representing another entity.
- 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_69f349d489d48190ba30e7d97c6f5ef9 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f7a225a77c81908f8953ccfeb14336 |
completed | May 3, 2026, 7:29 p.m. |
| PD | Predicate disambiguation | batch_69f7a06d4f108190bae3ab9ae431d2c7 |
completed | May 3, 2026, 7:22 p.m. |
Created at: May 1, 2026, 2:03 a.m.