Triple
T33329560
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 仁徳天皇 |
E853363
|
entity |
| Predicate | 宗教的側面 |
P2154
|
FINISHED |
| Object | 神格化され神社で祀られることがある |
—
|
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: 神格化され神社で祀られることがある | Statement: [仁徳天皇, 宗教的側面, 神格化され神社で祀られることがある]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 宗教的側面 Context triple: [仁徳天皇, 宗教的側面, 神格化され神社で祀られることがある]
-
A.
religiousSide
Indicates that one entity is aligned with, belongs to, or represents a particular religious faction, denomination, or side in a religious context.
-
B.
religiousElement
chosen
Indicates that something is a component, aspect, or feature associated with a religion or religious practice.
-
C.
religionNote
Indicates that there is an explanatory note or comment providing additional information or clarification about a subject’s religion.
-
D.
religiousSceneSpecialization
Indicates a relationship where a scene is specifically characterized or classified by a particular religious theme, context, or function.
-
E.
bearerReligion
Indicates that a bearer (such as a person or entity) adheres to, practices, or is associated with a particular religion.
- 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_69f34969614c81909cd99661b0902533 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6e3156ea48190b604e414665ef351 |
completed | May 3, 2026, 5:54 a.m. |
| PD | Predicate disambiguation | batch_69f6de0b9ba48190887c9eb5d06a2e94 |
completed | May 3, 2026, 5:32 a.m. |
Created at: May 1, 2026, 1:34 a.m.