Triple
T2303236
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jain nuns |
E51777
|
entity |
| Predicate | languageOfScriptureStudy |
P1187
|
FINISHED |
| Object | Prakrit (in traditional study) |
—
|
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: Prakrit (in traditional study) | Statement: [Jain nuns, languageOfScriptureStudy, Prakrit (in traditional study)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfScriptureStudy Context triple: [Jain nuns, languageOfScriptureStudy, Prakrit (in traditional study)]
-
A.
hasLanguageOfScripture
chosen
Indicates that an entity’s scriptural or sacred texts are written or expressed in a specified language.
-
B.
inScripture
Indicates that something is mentioned, described, or referenced within a scriptural or sacred text.
-
C.
scripturalCorpus
Indicates that one entity is a body of scriptural or sacred texts associated with, or serving as the canonical writings for, another entity.
-
D.
usedScriptureTranslation
Indicates that one entity employed or relied on a particular translation of scripture in its actions, works, or communications.
-
E.
scriptureType
Indicates the classification or category of a scripture in relation to its type or genre.
- 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_69a88b0a9f248190bcff941463d8f65a |
completed | March 4, 2026, 7:42 p.m. |
| NER | Named-entity recognition | batch_69abcbabf01081908db3b42bc7c60444 |
completed | March 7, 2026, 6:54 a.m. |
| PD | Predicate disambiguation | batch_69abc58ad33c8190b8d68af41b6f5e07 |
completed | March 7, 2026, 6:28 a.m. |
Created at: March 4, 2026, 7:49 p.m.