Triple
T20964736
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Valmiki Jayanti |
E516336
|
entity |
| Predicate | languageOfScripturesRecited |
P1187
|
FINISHED |
| Object | Sanskrit |
—
|
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: Sanskrit | Statement: [Valmiki Jayanti, languageOfScripturesRecited, Sanskrit]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfScripturesRecited Context triple: [Valmiki Jayanti, languageOfScripturesRecited, Sanskrit]
-
A.
hasLanguageOfScripture
chosen
Indicates that an entity’s scriptural or sacred texts are written or expressed in a specified language.
-
B.
recognizesScripturesFrom
Indicates that one entity acknowledges or accepts certain scriptures as authoritative or valid based on another entity as their source or origin.
-
C.
usedScriptureTranslation
Indicates that one entity employed or relied on a particular translation of scripture in its actions, works, or communications.
-
D.
religiousTextLanguageOf
Indicates that a particular language is the language in which a given religious text is written or primarily expressed.
-
E.
typicalLanguageOfReadings
Indicates the language that is most commonly used for readings or interpretations associated with a given 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_69e0b4fde6c48190af1398e7e734629e |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6fb71d644819087e00933fcc26217 |
completed | April 21, 2026, 4:22 a.m. |
| PD | Predicate disambiguation | batch_69e5dbe6976081908abd4e9c8734bae9 |
completed | April 20, 2026, 7:55 a.m. |
Created at: April 16, 2026, 1:32 p.m.