Triple
T21522086
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 明日香村 |
E530999
|
entity |
| Predicate | 地方区分 |
P128584
|
FINISHED |
| Object | 近畿地方 |
—
|
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: 近畿地方 | Statement: [明日香村, 地方区分, 近畿地方]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 地方区分 Context triple: [明日香村, 地方区分, 近畿地方]
-
A.
regionallyDistinctFrom
Indicates that two entities differ from each other in characteristics or classification based on their geographic or regional context.
-
B.
regionSpecialization
Indicates that a region is designated or recognized as being particularly focused on, adapted to, or specialized in a specific function, activity, or domain.
-
C.
regionalType
chosen
Indicates the classification of a region according to its designated type or category within a broader geographic or administrative system.
-
D.
regionOfCity
Indicates that a specified area or district is a constituent part or subdivision of a particular city.
-
E.
regionalCharacteristic
Indicates that a particular feature, quality, or attribute is typical of, or distinctive to, a specific geographic region.
- 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_69e0c45d95a081908e7962ad215da746 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ee884cff5881908d93a54578e7b1b0 |
completed | April 26, 2026, 9:49 p.m. |
| PD | Predicate disambiguation | batch_69e6320043bc81909417c41a718652ba |
completed | April 20, 2026, 2:02 p.m. |
Created at: April 16, 2026, 6:26 p.m.