Triple
T189369
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sanskrit |
E3683
|
entity |
| Predicate | influenced |
P9
|
FINISHED |
| Object | Gujarati |
E8599
|
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: Gujarati | Statement: [Sanskrit, influenced, Gujarati]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gujarati Context triple: [Sanskrit, influenced, Gujarati]
-
A.
Gujarati
chosen
Gujarati is an Indo-Aryan language primarily spoken in the Indian state of Gujarat and by Gujarati communities worldwide.
-
B.
Hindi
Hindi is an Indo-Aryan language widely spoken across northern and central India and used in government, education, media, and popular culture.
-
C.
Sindhi
Sindhi is an Indo-Aryan language spoken primarily in Pakistan and India, known for its rich literary tradition and distinct script variants.
-
D.
Maharashtri Prakrit
Maharashtri Prakrit is an ancient Middle Indo-Aryan language historically used in western and central India, especially in classical poetry and drama, and is a key ancestor of several modern Indo-Aryan languages.
-
E.
Maithili
Maithili is an Indo-Aryan language spoken primarily in the eastern Indian state of Bihar and neighboring regions, with a rich literary tradition and official recognition in India.
- 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_69a2548debd48190ae3a06d6e65b53c6 |
completed | Feb. 28, 2026, 2:35 a.m. |
| NER | Named-entity recognition | batch_69a2594c385481909e1e088e45c460a4 |
completed | Feb. 28, 2026, 2:56 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a31159301481909a3521339d2338fa |
completed | Feb. 28, 2026, 4:01 p.m. |
Created at: Feb. 28, 2026, 2:41 a.m.