Triple
T574410
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shikoku |
E13729
|
entity |
| Predicate | administrativeDivisionCount |
P4404
|
FINISHED |
| Object | 4 prefectures |
—
|
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: 4 prefectures | Statement: [Shikoku, administrativeDivisionCount, 4 prefectures]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: administrativeDivisionCount Context triple: [Shikoku, administrativeDivisionCount, 4 prefectures]
-
A.
hasNumberOfProvinces
Indicates the total count of provinces associated with a given entity.
-
B.
politicalDivision
Indicates that one entity is a governmental or administrative subdivision or jurisdiction within the territory or authority of another entity.
-
C.
numberOfProvinces
chosen
Indicates the total count of provinces associated with a given entity or within a specified region or country.
-
D.
numberOfRegions
Indicates the total count of distinct regions associated with or contained within a given entity.
-
E.
numberOfDistricts
Indicates the total count of districts associated with a given entity or area.
- 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_69a4933fa4d88190a7949cc83c08c5c1 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49b4c23548190a3b883239c7c78c8 |
completed | March 1, 2026, 8:02 p.m. |
| PD | Predicate disambiguation | batch_69a494c4969c819080375d08f9eec50c |
completed | March 1, 2026, 7:34 p.m. |
Created at: March 1, 2026, 7:33 p.m.