Triple
T23395340
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Berar region |
E559341
|
entity |
| Predicate | majorCity |
P316
|
FINISHED |
| Object | Buldhana |
—
|
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: Buldhana | Statement: [Berar region, majorCity, Buldhana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Buldhana Context triple: [Berar region, majorCity, Buldhana]
-
A.
Buldhana
chosen
Buldhana is a district in the Indian state of Maharashtra known for its Marathi-speaking population and use of the Varhadi dialect.
-
B.
Hingoli
Hingoli is a city in the Indian state of Maharashtra, known as an administrative and commercial center in the Marathwada region.
-
C.
Ratnagiri
Ratnagiri is a coastal city in Maharashtra, India, known for its Alphonso mangoes, historic forts, and scenic beaches along the Konkan coast.
-
D.
Bhusawal
Bhusawal is a major railway and commercial city in Maharashtra, India, known for its large railway junction and banana-growing region.
-
E.
Malegaon
Malegaon is a major textile and powerloom town in Maharashtra, India, known for its large Muslim population and vibrant weaving industry.
- 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_69e24549610c8190a069d6411ce5f661 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1a4db1c888190ace5d58bcc8645c1 |
completed | April 29, 2026, 6:27 a.m. |
Created at: April 17, 2026, 5:36 p.m.