Triple
T29794625
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 豊郷町 |
E756511
|
entity |
| Predicate | 教育施設の特徴 |
P17044
|
FINISHED |
| Object | 歴史的建造物を活用した施設がある |
—
|
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: 歴史的建造物を活用した施設がある | Statement: [豊郷町, 教育施設の特徴, 歴史的建造物を活用した施設がある]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 教育施設の特徴 Context triple: [豊郷町, 教育施設の特徴, 歴史的建造物を活用した施設がある]
-
A.
educationSystemCharacteristic
chosen
Indicates a characteristic, feature, or attribute that describes an education system.
-
B.
featuresInstitution
Indicates that one entity includes, presents, or highlights an institution as a notable component or participant.
-
C.
educationFacility
Indicates that one entity functions as an institution or place where the other entity receives or provides education or training.
-
D.
hasEducationFacilities
Indicates that an entity possesses or provides educational institutions, services, or infrastructure for learning and training.
-
E.
universityCharacteristic
Indicates that a specified characteristic, quality, or attribute is associated with a particular university.
- 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_69f22454583081908927516cb9938d1d |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f674e5cf0c81908f779723cf602daf |
completed | May 2, 2026, 10:04 p.m. |
| PD | Predicate disambiguation | batch_69f66ac1a4fc81909740d2e52fbe6970 |
completed | May 2, 2026, 9:21 p.m. |
Created at: April 29, 2026, 5:14 p.m.