Triple
T7302474
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Balurghat Airport |
E167890
|
entity |
| Predicate | serves |
P98
|
FINISHED |
| Object | Balurghat |
E184768
|
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: Balurghat | Statement: [Balurghat Airport, serves, Balurghat]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Balurghat Context triple: [Balurghat Airport, serves, Balurghat]
-
A.
Balurghat
chosen
Balurghat is a town and district headquarters in the Dakshin Dinajpur district of the Indian state of West Bengal, known for its cultural heritage and proximity to the India–Bangladesh border.
-
B.
Rampurhat
Rampurhat is a town and important railway junction in the Birbhum district of West Bengal, India.
-
C.
Sainthia
Sainthia is a town in the Birbhum district of West Bengal, India, known as a local commercial and cultural center.
-
D.
Santipur
Santipur is a historic town in West Bengal, India, renowned for its traditional handloom sarees and cultural heritage.
-
E.
Begusarai
Begusarai is an industrial and agricultural city in the Indian state of Bihar, known for its oil refinery and role as a regional commercial hub.
- 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_69c6888c820881909fc68f689fe1c251 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6ebb2261c8190ae9095c8e110b528 |
completed | March 27, 2026, 8:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c90092f9ac8190a53d65cfdeafca29 |
completed | March 29, 2026, 10:36 a.m. |
Created at: March 27, 2026, 3:01 p.m.