Triple
T1690259
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Article 47 of the Constitution of Japan |
E36534
|
entity |
| Predicate | belongsToChapter |
P6720
|
FINISHED |
| Object | Chapter IV: The Diet |
—
|
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: Chapter IV: The Diet | Statement: [Article 47 of the Constitution of Japan, belongsToChapter, Chapter IV: The Diet]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: belongsToChapter Context triple: [Article 47 of the Constitution of Japan, belongsToChapter, Chapter IV: The Diet]
-
A.
containsChapter
chosen
Indicates that one entity (typically a larger work or document) includes another entity as a chapter within its structure.
-
B.
belongsToChamber
Indicates that an entity is a member of, or is formally associated with, a specific chamber or legislative body.
-
C.
containsSubchapter
Indicates that one chapter or section includes another, more specific subchapter as a part of its structure.
-
D.
belongsToProgram
Indicates that an entity is a member of, or is associated with, a specific program.
-
E.
hasLocalChaptersIn
Indicates that an organization maintains one or more local chapters or branches within a specified geographic area or location.
- 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_69a886151508819084fa7f1ce6e05577 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aaf3359ce48190803b322db8ad6027 |
completed | March 6, 2026, 3:31 p.m. |
| PD | Predicate disambiguation | batch_69aa61b71cec8190b273588051058ebd |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:29 p.m.