Triple
T25096079
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 熊本市 |
E628592
|
entity |
| Predicate | 歴史的特徴 |
P18777
|
FINISHED |
| Object | 武家文化の中心地 |
—
|
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: 武家文化の中心地 | Statement: [熊本市, 歴史的特徴, 武家文化の中心地]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 歴史的特徴 Context triple: [熊本市, 歴史的特徴, 武家文化の中心地]
-
A.
historicalCharacteristic
chosen
Indicates that an entity possesses a trait, feature, or quality that is rooted in or defined by its history or past events.
-
B.
strategicFeatureHistorical
Indicates that something has historically served as a strategic feature or asset within a broader plan, conflict, or competitive context.
-
C.
historicalType
Indicates that one entity classifies or characterizes another in terms of its role, status, or category within a historical context.
-
D.
featuresHistorian
Indicates that something includes or presents a historian as a notable participant, subject, or element.
-
E.
hasHistoricFeatures
Indicates that something possesses characteristics, elements, or attributes of historical significance.
- 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_69e2ff2f58e881908340527bc5d34f07 |
completed | April 18, 2026, 3:49 a.m. |
| NER | Named-entity recognition | batch_69f464b9651481908d4d7584717f5c59 |
completed | May 1, 2026, 8:30 a.m. |
| PD | Predicate disambiguation | batch_69f44d8043b081908bbffd7f044b4f26 |
completed | May 1, 2026, 6:51 a.m. |
Created at: April 18, 2026, 6:25 a.m.