Triple
T37061185
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 関西大学高槻キャンパス |
E917327
|
entity |
| Predicate | 地理的区分 |
P171829
|
FINISHED |
| Object | 近畿地方 |
—
|
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: 近畿地方 | Statement: [関西大学高槻キャンパス, 地理的区分, 近畿地方]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 地理的区分 Context triple: [関西大学高槻キャンパス, 地理的区分, 近畿地方]
-
A.
geographicalStructure
Indicates a relationship where one entity is a physical geographic feature or formation that characterizes or shapes the spatial nature of another entity or area.
-
B.
ردهٔجغرافیایی
chosen
Indicates a geographical classification or categorization relationship between entities based on their location or spatial grouping.
-
C.
naturalRegionType
Indicates the type or category of natural region to which a given area or place belongs.
-
D.
hasGeographyCharacteristic
Indicates that an entity possesses a specific geographical feature, property, or attribute.
-
E.
geographicalRegionType
Indicates the specific kind or category of geographical region that an entity belongs to (e.g., continent, country, province, or city).
- 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_69f76e95fa40819091e14681087ae5e4 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fb34e5576881909394355c8ec6ddd2 |
completed | May 6, 2026, 12:32 p.m. |
| PD | Predicate disambiguation | batch_69fb2f6171e88190bf1e0ee6a644b6a9 |
completed | May 6, 2026, 12:09 p.m. |
Created at: May 3, 2026, 4:14 p.m.