Triple
T968138
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shema Yisrael |
E20882
|
entity |
| Predicate | translationEn |
P21151
|
FINISHED |
| Object | Hear, O Israel: the Lord is our God, the Lord is One |
—
|
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: Hear, O Israel: the Lord is our God, the Lord is One | Statement: [Shema Yisrael, translationEn, Hear, O Israel: the Lord is our God, the Lord is One]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: translationEn Context triple: [Shema Yisrael, translationEn, Hear, O Israel: the Lord is our God, the Lord is One]
-
A.
translationApproximate
Indicates that one entity is an inexact or approximate translation of another, preserving general meaning but not precise wording or full detail.
-
B.
translationDirection
Indicates the source and target languages involved in a translation, specifying the direction from the original language to the translated language.
-
C.
translationMethod
Indicates the technique or process used to translate content from one language or form to another.
-
D.
translationTargetLanguage
chosen
Indicates the language into which content is being or has been translated.
-
E.
translator
Indicates that one entity serves to convert or render content from one language or form into another for a second entity.
- 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_69a493b33d2c81909c52c369d3ca8436 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b43549008190a4d65efdc3bda520 |
completed | March 1, 2026, 9:48 p.m. |
| PD | Predicate disambiguation | batch_69a4b2a579888190afb489ac9fe8391c |
completed | March 1, 2026, 9:41 p.m. |
Created at: March 1, 2026, 7:40 p.m.