Triple
T186456
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nihon-koku Kenpō |
E3991
|
entity |
| Predicate | containsChapter |
P6720
|
FINISHED |
| Object | Chapter I: The Emperor |
—
|
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 I: The Emperor | Statement: [Nihon-koku Kenpō, containsChapter, Chapter I: The Emperor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: containsChapter Context triple: [Nihon-koku Kenpō, containsChapter, Chapter I: The Emperor]
-
A.
numberOfChapters
Indicates the total count of chapters associated with a given entity.
-
B.
containsArticle
Indicates that one entity includes or holds an article (such as a written piece, item, or document) as part of its contents.
-
C.
containsTitle
Indicates that one entity includes or holds another entity’s title as part of its content or metadata.
-
D.
hasCompanionBook
Indicates that one entity (typically a primary work) is associated with another entity that serves as its companion book, providing supplementary or related content.
-
E.
hasSequel
Indicates that one work is followed by another work that continues its story, timeline, or thematic development.
- F. None of above. chosen
Provenance (4 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_69a25497e2f08190a040f8c6e1842643 |
completed | Feb. 28, 2026, 2:36 a.m. |
| NER | Named-entity recognition | batch_69a2594809288190b3d3b1283e7e0d00 |
completed | Feb. 28, 2026, 2:56 a.m. |
| PD | Predicate disambiguation | batch_69a25670feb081908e26a2543ebe7b3a |
completed | Feb. 28, 2026, 2:44 a.m. |
| PDg | Predicate description generation | batch_69a257e763d081908c54ad57d8d3060d |
completed | Feb. 28, 2026, 2:50 a.m. |
Created at: Feb. 28, 2026, 2:40 a.m.