Triple
T38018406
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dharmaguptaka |
E948555
|
entity |
| Predicate | bhikshuniPreceptsCount |
P189967
|
FINISHED |
| Object | 348 precepts |
—
|
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: 348 precepts | Statement: [Dharmaguptaka, bhikshuniPreceptsCount, 348 precepts]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bhikshuniPreceptsCount Context triple: [Dharmaguptaka, bhikshuniPreceptsCount, 348 precepts]
-
A.
bhikshuPreceptsCount
Indicates the number of monastic precepts that a bhikshu (fully ordained monk) is bound to observe.
-
B.
numberOfSutras
Indicates the quantity or count of sutras associated with a given entity.
-
C.
numberOfMonks
Indicates the quantity or count of monks associated with a given entity or context.
-
D.
vinayaPreservedIn
Indicates that a body of Vinaya (monastic disciplinary rules) is preserved, transmitted, or contained within a particular text, collection, or tradition.
-
E.
numberOfMonksApprox
Indicates an approximate count or estimate of how many monks are involved or present 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_69f76efc10448190aff5fb566b98f952 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fc4748843c8190931432653be4890c |
completed | May 7, 2026, 8:03 a.m. |
| PD | Predicate disambiguation | batch_69fc45646ce481908caf292ff9f06e15 |
completed | May 7, 2026, 7:55 a.m. |
| PDg | Predicate description generation | batch_69fc4747b06c8190a3ea5331f02eedad |
completed | May 7, 2026, 8:03 a.m. |
Created at: May 3, 2026, 4:20 p.m.