Triple
T4519774
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vyasa |
E103237
|
entity |
| Predicate | textualRoleInMahabharata |
P11527
|
FINISHED |
| Object | author |
—
|
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: author | Statement: [Vyasa, textualRoleInMahabharata, author]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: textualRoleInMahabharata Context triple: [Vyasa, textualRoleInMahabharata, author]
-
A.
roleInText
chosen
Indicates that an entity participates in a text with a specific function or capacity (e.g., author, editor, character).
-
B.
roleInWarAndPeace
Indicates that an entity has a specific role or function within the context of the War and Peace conflict or narrative.
-
C.
featuresCharacterRole
Indicates that a work includes a character appearing in a specific narrative or functional role.
-
D.
mythologicalRole
Indicates the specific function, duty, or status an entity holds within a mythological or legendary context.
-
E.
biblicalFigureRole
Indicates the specific role, function, or office that a biblical figure holds within a biblical narrative or tradition.
- 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_69bd43dba59881908cf59b31df8c7ae1 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd5747e90c81908fa112ecace699a9 |
completed | March 20, 2026, 2:18 p.m. |
| PD | Predicate disambiguation | batch_69bd521abea48190b3e758a1f98dd55e |
completed | March 20, 2026, 1:56 p.m. |
Created at: March 20, 2026, 1:02 p.m.