Triple
T13564725
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Linus van Pelt |
E324004
|
entity |
| Predicate | religiousKnowledge |
P47348
|
FINISHED |
| Object | Christian theology |
—
|
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: Christian theology | Statement: [Linus van Pelt, religiousKnowledge, Christian theology]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: religiousKnowledge Context triple: [Linus van Pelt, religiousKnowledge, Christian theology]
-
A.
religiousTopicAddressed
chosen
Indicates that a subject deals with, discusses, or focuses on a religious theme, issue, or question.
-
B.
religiousElement
Indicates that something is a component, aspect, or feature associated with a religion or religious practice.
-
C.
religionCommon
Indicates that the related entities share the same religion or religious affiliation.
-
D.
religiousTextCategory
Indicates that a religious text belongs to or is classified under a particular category or type.
-
E.
religiousFunction
Indicates that one entity serves a religious role, purpose, or function in relation to another entity.
- 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_69d8076830b48190910a902bae5888e2 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb00cecd48190a9a2caff3d424817 |
completed | April 12, 2026, 2:45 p.m. |
| PD | Predicate disambiguation | batch_69dbae161a0481909f9d3f40ca4e0ac5 |
completed | April 12, 2026, 2:37 p.m. |
Created at: April 9, 2026, 9:47 p.m.