Triple
T38661247
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kāśyapīya school |
E940339
|
entity |
| Predicate | hasTextualGenre |
P22130
|
FINISHED |
| Object | Sūtra |
—
|
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: Sūtra | Statement: [Kāśyapīya school, hasTextualGenre, Sūtra]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTextualGenre Context triple: [Kāśyapīya school, hasTextualGenre, Sūtra]
-
A.
hasGenreInFiction
Indicates that a work of fiction belongs to or is categorized under a specific literary genre.
-
B.
hasTextualCorpus
Indicates that an entity is associated with or possesses a collection of written or textual materials.
-
C.
hasTextualTransmission
Indicates that a work, idea, or content has been passed down, preserved, or conveyed through written or textual forms over time.
-
D.
hasTextualCharacter
Indicates that something possesses or exhibits the qualities of written or printed text, such as letters, symbols, or characters.
-
E.
literaryGenreOfWork
chosen
Indicates that a work belongs to or is classified under a particular literary 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_69f76edfde348190bf6529d9f49ecd62 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fee0b2da3c8190a3519d0564f2f32d |
completed | May 9, 2026, 7:22 a.m. |
| PD | Predicate disambiguation | batch_69fee05b315c819081dfcbfb15273487 |
completed | May 9, 2026, 7:20 a.m. |
Created at: May 3, 2026, 4:33 p.m.