Triple
T21523651
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Christopher Chessun |
E531037
|
entity |
| Predicate | ordainedDeaconIn |
P111338
|
FINISHED |
| Object | 1983 |
—
|
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: 1983 | Statement: [Christopher Chessun, ordainedDeaconIn, 1983]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ordainedDeaconIn Context triple: [Christopher Chessun, ordainedDeaconIn, 1983]
-
A.
ordainedIn
Indicates that an individual was formally appointed or consecrated to a religious office or role at a specific place or within a particular institution.
-
B.
ordainedFor
Indicates that one entity has been formally appointed or consecrated to serve, function, or act on behalf of another entity or purpose.
-
C.
ordainedBy
Indicates that an entity has been formally appointed, consecrated, or invested with authority by another entity.
-
D.
wasOrdainedAs
Indicates that an entity was formally appointed or consecrated into an official religious or ceremonial role.
-
E.
dateOfDiaconate
chosen
Indicates the date on which an individual was ordained to the diaconate.
- 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_69e0c45d95a081908e7962ad215da746 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ee884f4504819086bd632e62f02f58 |
completed | April 26, 2026, 9:49 p.m. |
| PD | Predicate disambiguation | batch_69e6320043bc81909417c41a718652ba |
completed | April 20, 2026, 2:02 p.m. |
Created at: April 16, 2026, 6:26 p.m.