Triple
T20964726
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Valmiki Jayanti |
E516336
|
entity |
| Predicate | dedicatedTo |
P500
|
FINISHED |
| Object | sage Valmiki |
—
|
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: sage Valmiki | Statement: [Valmiki Jayanti, dedicatedTo, sage Valmiki]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: sage Valmiki Context triple: [Valmiki Jayanti, dedicatedTo, sage Valmiki]
-
A.
Valmiki
chosen
Valmiki is the revered ancient Indian sage traditionally credited with composing the epic Sanskrit poem Ramayana.
-
B.
sage Asit Muni
Sage Asit Muni is an ancient Hindu ascetic revered in mythology for his deep penance and spiritual association with the Himalayan source of the Yamuna River.
-
C.
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.
-
D.
Vyasa
Vyasa is the legendary sage in Hindu tradition credited with composing and compiling the Mahabharata and organizing the Vedas.
-
E.
Kumara Vyasa
Kumara Vyasa was a renowned medieval Kannada poet best known for his epic retelling of the Mahabharata, "Karnata Bharata Kathamanjari."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4fde6c48190af1398e7e734629e |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6fb71d644819087e00933fcc26217 |
completed | April 21, 2026, 4:22 a.m. |
Created at: April 16, 2026, 1:32 p.m.