Triple
T7296357
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Krishnanagar Sadar subdivision |
E164530
|
entity |
| Predicate | hasUrbanArea |
P316
|
FINISHED |
| Object | Ranaghat |
E676449
|
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: Ranaghat | Statement: [Krishnanagar Sadar subdivision, hasUrbanArea, Ranaghat]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ranaghat Context triple: [Krishnanagar Sadar subdivision, hasUrbanArea, Ranaghat]
-
A.
Ranaghat
chosen
Ranaghat is a prominent town in the Indian state of West Bengal, known as a key railway junction and commercial center in the Nadia region.
-
B.
Raiganj
Raiganj is a town in northern West Bengal, India, known as the headquarters of Uttar Dinajpur district and for its nearby Raiganj Wildlife Sanctuary.
-
C.
Baranagar
Baranagar is a densely populated suburban city in the northern part of Kolkata, India, known for its industrial areas, educational institutions, and cultural heritage.
-
D.
Sainthia
Sainthia is a town in the Birbhum district of West Bengal, India, known as a local commercial and cultural center.
-
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_69c6887a499881909dd23341399c59d8 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6eb8e48d48190ada4d507f3b61bc4 |
completed | March 27, 2026, 8:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8be10c5b081908210981ce9c45bd9 |
completed | March 29, 2026, 5:52 a.m. |
Created at: March 27, 2026, 3 p.m.