Triple
T21944842
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Astitva |
E541908
|
entity |
| Predicate | editor |
P1954
|
FINISHED |
| Object | V. N. Mayekar |
—
|
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: V. N. Mayekar | Statement: [Astitva, editor, V. N. Mayekar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: V. N. Mayekar Context triple: [Astitva, editor, V. N. Mayekar]
-
A.
V. N. Mayekar
chosen
V. N. Mayekar is an editor known for his work on the Indian film "Taal."
-
B.
G. G. Mayekar
G. G. Mayekar was a film editor known for his work in Indian cinema, including editing the acclaimed Hindi film "Aandhi."
-
C.
V. S. Khandekar
V. S. Khandekar was a prominent 20th-century Indian writer best known for his influential Marathi novels and short stories, including the Jnanpith Award-winning novel "Yayati."
-
D.
S. K. Belvalkar
S. K. Belvalkar was an Indian Sanskrit scholar and Indologist known for his significant contributions to textual criticism and editions of classical Indian epics and philosophical works.
-
E.
R. N. Dandekar
R. N. Dandekar was a prominent Indian Indologist and Sanskrit scholar known for his significant contributions to Vedic and epic studies.
- 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_69e0c47e2e5c81909a7f74ce3de50911 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f1242688988190a7b8f033c49368de |
completed | April 28, 2026, 9:18 p.m. |
Created at: April 16, 2026, 7:56 p.m.