Triple
T20080959
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RajasuyaYajna |
E499998
|
entity |
| Predicate | languageOfScripturalDescription |
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: [RajasuyaYajna, languageOfScripturalDescription, Sanskrit]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfScripturalDescription Context triple: [RajasuyaYajna, languageOfScripturalDescription, Sanskrit]
-
A.
hasLanguageOfScripture
chosen
Indicates that an entity’s scriptural or sacred texts are written or expressed in a specified language.
-
B.
associatedLanguageScript
Indicates that there is a relationship between a language and the script or writing system used to represent it.
-
C.
typicalLanguageOfReadings
Indicates the language that is most commonly used for readings or interpretations associated with a given entity.
-
D.
languageOfScriptPromoted
Indicates that a particular language is associated with and promoted through the use of a given writing script.
-
E.
scripturalLanguageName
Indicates the name of the language in which a given scripture or sacred text is written.
- 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_69da627770948190997f486f9a2e370f |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66557c19c8190b511857490bbd423 |
completed | April 20, 2026, 5:41 p.m. |
| PD | Predicate disambiguation | batch_69e54cf369b88190931532420517dac7 |
completed | April 19, 2026, 9:45 p.m. |
Created at: April 11, 2026, 3:41 p.m.