Triple
T5675042
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Great Ektenia |
E125063
|
entity |
| Predicate | typicalOpeningWords |
P829
|
FINISHED |
| Object | In peace let us pray to the Lord |
—
|
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: In peace let us pray to the Lord | Statement: [Great Ektenia, typicalOpeningWords, In peace let us pray to the Lord]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalOpeningWords Context triple: [Great Ektenia, typicalOpeningWords, In peace let us pray to the Lord]
-
A.
openingVerb
Indicates that an entity performs the initial or primary action that begins an event, process, or interaction.
-
B.
openingCatchphrase
Indicates that one entity is a characteristic phrase or line regularly used by another entity at the beginning of a recurring performance, appearance, or communication.
-
C.
openingLine
chosen
Indicates that one entity is the first line or initial statement that begins another entity, such as a text, speech, or conversation.
-
D.
translationOfOpeningWords
Indicates that one text is a translation of the initial words or opening phrase of another text.
-
E.
typicalGreeting
Indicates the standard or commonly used way one entity greets another in a given context.
- 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_69c008295c808190acfe78915e7d656a |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c025303860819093e51f176babed71 |
completed | March 22, 2026, 5:21 p.m. |
| PD | Predicate disambiguation | batch_69c021bc3894819084f37d14ba4b2644 |
completed | March 22, 2026, 5:07 p.m. |
Created at: March 22, 2026, 3:43 p.m.