Triple
T24935102
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toyotomi Japan |
E623290
|
entity |
| Predicate | toleratesReligion |
P36791
|
FINISHED |
| Object | Christianity |
—
|
NE NERFINISHED |
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: Christianity | Statement: [Toyotomi Japan, toleratesReligion, Christianity]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: toleratesReligion Context triple: [Toyotomi Japan, toleratesReligion, Christianity]
-
A.
religiousAttitude
chosen
Indicates an entity’s stance, disposition, or orientation toward religion or religious beliefs.
-
B.
recognizedReligionPolicy
Indicates that a governing authority has an official policy specifying which religions are formally recognized and how they are treated or regulated.
-
C.
religionNeutral
Indicates that the relationship or action is independent of, unaffected by, or not characterized in terms of any particular religion or religious affiliation.
-
D.
hasReligious
Indicates that an entity is associated with, practices, or adheres to a particular religion or religious affiliation.
-
E.
allowsReligiousParticipation
Indicates that one entity grants another entity the permission or opportunity to take part in religious activities, practices, or institutions.
- 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_69e2fac6b5a48190a1c38857f00915a9 |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f423d4043c8190952356417e9b504c |
completed | May 1, 2026, 3:53 a.m. |
| PD | Predicate disambiguation | batch_69f4210130d08190ae30b7943f7a0bbc |
completed | May 1, 2026, 3:41 a.m. |
Created at: April 18, 2026, 5:30 a.m.