Triple
T4738799
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ashramavasika Parva |
E105187
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object | Vyasa |
E103237
|
NE 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: Vyasa | Statement: [Ashramavasika Parva, associatedWith, Vyasa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vyasa Context triple: [Ashramavasika Parva, associatedWith, Vyasa]
-
A.
Vyasa
chosen
Vyasa is the legendary sage in Hindu tradition credited with composing and compiling the Mahabharata and organizing the Vedas.
-
B.
Vaishampayana
Vaishampayana is an ancient sage and disciple of Vyasa, best known in Hindu tradition for reciting and transmitting the Mahabharata.
-
C.
Valmiki
Valmiki is the revered ancient Indian sage traditionally credited with composing the epic Sanskrit poem Ramayana.
-
D.
Bharata Muni
Bharata Muni is the ancient Indian sage traditionally credited with authoring the Nāṭyaśāstra, the foundational treatise on Sanskrit dramaturgy, performance, and aesthetic theory.
-
E.
Tulsidas
Tulsidas was a 16th-century Indian poet-saint best known for composing the epic Ramcharitmanas, a retelling of the Ramayana in the vernacular that deeply influenced North Indian devotional culture.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69bd43ef87a48190a5bc3600711aa032 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6485d690819098dc4a974516da6b |
completed | March 20, 2026, 3:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be6f979ca88190bc0128812329cb24 |
completed | March 21, 2026, 10:14 a.m. |
Created at: March 20, 2026, 1:19 p.m.