Triple
T36129003
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 佐賀城 |
E1044962
|
entity |
| Predicate | 観光地種別 |
P1769
|
FINISHED |
| Object | 歴史観光地 |
—
|
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: 歴史観光地 | Statement: [佐賀城, 観光地種別, 歴史観光地]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 観光地種別 Context triple: [佐賀城, 観光地種別, 歴史観光地]
-
A.
tourismType
chosen
Indicates the specific category or kind of tourism activity or experience associated with an entity.
-
B.
countryTourismCategory
Indicates the tourism classification or category assigned to a country based on its tourism characteristics or status.
-
C.
typeOfLandmark
Indicates the specific category or kind of landmark that an entity belongs to (e.g., monument, natural feature, building).
-
D.
tourismFeature
Indicates that something serves as an attraction, amenity, or point of interest relevant to tourism or visitors.
-
E.
tourismCharacteristic
Indicates that something has a specific feature, quality, or attribute relevant to tourism, such as what makes a place, service, or activity notable or suitable for tourists.
- 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_69f76e356c908190abc6ca1e6a05b011 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7b3e2f3c08190be4fd1ae4fa1266d |
completed | May 3, 2026, 8:45 p.m. |
| PD | Predicate disambiguation | batch_69f7b1bcc47081909fe7d592ac69006c |
completed | May 3, 2026, 8:36 p.m. |
Created at: May 3, 2026, 4:08 p.m.