Triple
T4463817
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sheer Thursday |
E98323
|
entity |
| Predicate | hasCommandmentTheme |
P13857
|
FINISHED |
| Object | "new commandment" to love one another |
—
|
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: "new commandment" to love one another | Statement: [Sheer Thursday, hasCommandmentTheme, "new commandment" to love one another]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCommandmentTheme Context triple: [Sheer Thursday, hasCommandmentTheme, "new commandment" to love one another]
-
A.
commandmentType
Indicates the specific category or kind of commandment that an instruction or directive belongs to.
-
B.
numberOfCommandments
Indicates the total count of commandments associated with a given subject.
-
C.
hasReligiousTheme
Indicates that something (such as a work, event, or object) centrally involves or expresses religious ideas, symbols, practices, or narratives.
-
D.
commandmentHebrewName
Indicates the Hebrew-language name assigned to a particular commandment.
-
E.
scripturalTheme
chosen
Indicates that one entity represents a central religious or theological theme expressed, discussed, or emphasized within a scriptural text or passage.
- 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_69b3454a7c608190944f5455c8031d73 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3567913788190ac4f28fbe63a4fa9 |
completed | March 13, 2026, 12:12 a.m. |
| PD | Predicate disambiguation | batch_69b34f65f6448190abfadb2ae5658798 |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:34 p.m.