Triple
T6443
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Episcopal Church |
E129
|
entity |
| Predicate | orientation |
P1101
|
FINISHED |
| Object | via media |
—
|
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: via media | Statement: [Episcopal Church, orientation, via media]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: orientation Context triple: [Episcopal Church, orientation, via media]
-
A.
status
Indicates the current condition, state, or standing of an entity within a given context.
-
B.
purpose
Indicates that one entity exists, is done, or is used in order to achieve, support, or serve the goal, function, or intended outcome of another entity.
-
C.
near
Indicates that one entity is located at a short distance from another entity in space or position.
-
D.
context
Indicates that one entity provides the surrounding circumstances, setting, or background within which another entity, event, or statement occurs or is interpreted.
-
E.
theme
Indicates the entity that is the primary participant or content affected or characterized by an action, event, or state.
- F. None of above. chosen
Provenance (4 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_69a23bb612708190b09f25385e4b63d1 |
completed | Feb. 28, 2026, 12:49 a.m. |
| NER | Named-entity recognition | batch_69a2421836f08190b54fc40edeb1a96b |
completed | Feb. 28, 2026, 1:17 a.m. |
| PD | Predicate disambiguation | batch_69a23fe064c881909496fd0e6b0e18d7 |
completed | Feb. 28, 2026, 1:07 a.m. |
| PDg | Predicate description generation | batch_69a242174cd4819095f78b221e1b41f1 |
completed | Feb. 28, 2026, 1:17 a.m. |
Created at: Feb. 28, 2026, 12:54 a.m.