Triple
T33324228
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ys |
E853220
|
entity |
| Predicate | religiousOverlay |
P80272
|
FINISHED |
| Object | Christianized legend |
—
|
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: Christianized legend | Statement: [Ys, religiousOverlay, Christianized legend]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: religiousOverlay Context triple: [Ys, religiousOverlay, Christianized legend]
-
A.
religiousElement
Indicates that something is a component, aspect, or feature associated with a religion or religious practice.
-
B.
religiousFunction
Indicates that one entity serves a religious role, purpose, or function in relation to another entity.
-
C.
religiousCounterpart
Indicates that one entity serves as the religious equivalent or analogue of another entity within a different belief system or tradition.
-
D.
religiousTarget
Indicates that an action, policy, or behavior is directed at someone or something specifically because of their religion or religious affiliation.
-
E.
religiousSceneSpecialization
chosen
Indicates a relationship where a scene is specifically characterized or classified by a particular religious theme, context, or function.
- 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_69f349685f088190b8fda44083a018a9 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69ff1c91bbac8190b84012dee1cb3b2c |
completed | May 9, 2026, 11:37 a.m. |
| PD | Predicate disambiguation | batch_69ff1c23ca508190bb5a435d765b7e53 |
completed | May 9, 2026, 11:36 a.m. |
Created at: May 1, 2026, 1:33 a.m.