Triple
T31942950
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kanjizai |
E815574
|
entity |
| Predicate | scriptureLanguageName |
P75413
|
FINISHED |
| Object | Avalokiteśvara (Sanskrit) |
—
|
NE NERFINISHED |
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: Avalokiteśvara (Sanskrit) | Statement: [Kanjizai, scriptureLanguageName, Avalokiteśvara (Sanskrit)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: scriptureLanguageName Context triple: [Kanjizai, scriptureLanguageName, Avalokiteśvara (Sanskrit)]
-
A.
scripturalLanguageName
chosen
Indicates the name of the language in which a given scripture or sacred text is written.
-
B.
scriptureLanguageRegister
Indicates the specific linguistic register or style in which a piece of scripture is expressed (e.g., formal, liturgical, vernacular).
-
C.
hasLanguageOfScripture
Indicates that an entity’s scriptural or sacred texts are written or expressed in a specified language.
-
D.
scriptureCategoryTranslated
Indicates that a scripture’s category has been rendered into another language.
-
E.
usedScriptureTranslation
Indicates that one entity employed or relied on a particular translation of scripture in its actions, works, or communications.
- 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_69f348f42d188190a33fc8d20ec50517 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f7bbf906d8819099020e548dd56bc9 |
completed | May 3, 2026, 9:19 p.m. |
| PD | Predicate disambiguation | batch_69f7b9a2dcf88190a7c9e109e41267be |
completed | May 3, 2026, 9:09 p.m. |
Created at: May 1, 2026, 12:06 a.m.