Triple
T19897463
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Supriya Sule |
E478186
|
entity |
| Predicate | residence |
P75
|
FINISHED |
| Object | Baramati |
—
|
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: Baramati | Statement: [Supriya Sule, residence, Baramati]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Baramati Context triple: [Supriya Sule, residence, Baramati]
-
A.
Baramati
chosen
Baramati is a town in the Pune district of Maharashtra, India, known as an agricultural and industrial hub with historical and political significance.
-
B.
Warora
Warora is a town in Maharashtra, India, known historically for its coal mining and industrial activities within the Chandrapur district.
-
C.
Bhusawal
Bhusawal is a major railway and commercial city in Maharashtra, India, known for its large railway junction and banana-growing region.
-
D.
Malegaon
Malegaon is a major textile and powerloom town in Maharashtra, India, known for its large Muslim population and vibrant weaving industry.
-
E.
Bhiwapur
Bhiwapur is a town in Maharashtra, India, known for its chili production and location within the Nagpur district.
- 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_69d8e520682081909892916424699bd5 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6593ef8dc8190be5b64988eceafac |
completed | April 20, 2026, 4:50 p.m. |
Created at: April 10, 2026, 1:52 p.m.