Triple
T3461252
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Avinu Malkeinu |
E73028
|
entity |
| Predicate | variantCount |
P48909
|
FINISHED |
| Object | dozens of individual petitions in some rites |
—
|
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: dozens of individual petitions in some rites | Statement: [Avinu Malkeinu, variantCount, dozens of individual petitions in some rites]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: variantCount Context triple: [Avinu Malkeinu, variantCount, dozens of individual petitions in some rites]
-
A.
colorVarietyCount
Indicates the number of distinct colors associated with or present in a given entity or set of entities.
-
B.
variant
Indicates that one entity is an alternative form, version, or variation of another entity.
-
C.
numberOfCounts
Indicates the total quantity or tally of discrete occurrences, items, or instances associated with an entity or event.
-
D.
numberOfPrimaryVarieties
Indicates the count of distinct primary varieties associated with a given entity.
-
E.
verseCountType
Indicates the type or categorization of how verses are counted or measured in a given context.
- 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_69ad85b224d481908ff8be51338d24ff |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adbae745e081909007cd3a664c57f3 |
completed | March 8, 2026, 6:07 p.m. |
| PD | Predicate disambiguation | batch_69adae05bb0081909dc7e4779d6e05ef |
completed | March 8, 2026, 5:12 p.m. |
| PDg | Predicate description generation | batch_69adb1ecb02881908394f197e31431b4 |
completed | March 8, 2026, 5:29 p.m. |
Created at: March 8, 2026, 3:17 p.m.