Triple
T22162041
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mymensingh Railway Station |
E547693
|
entity |
| Predicate | connectsTo |
P845
|
FINISHED |
| Object | Jamalpur |
—
|
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: Jamalpur | Statement: [Mymensingh Railway Station, connectsTo, Jamalpur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jamalpur Context triple: [Mymensingh Railway Station, connectsTo, Jamalpur]
-
A.
Jamalpur
chosen
Jamalpur is a city in central Bangladesh known as an important regional hub for agriculture and trade near the Jamuna River.
-
B.
Chapainawabganj
Chapainawabganj is a district town in western Bangladesh known for its mango production and location near the border with India.
-
C.
Kishoreganj
Kishoreganj is a town and district headquarters in central Bangladesh known for its agricultural economy, riverine landscape, and cultural heritage.
-
D.
Joypurhat
Joypurhat is a district-level town in northern Bangladesh known for its agricultural economy and location within the Rajshahi Division.
-
E.
Lakhipur
Lakhipur is a notable town in the Indian state of Assam, recognized as one of the main urban centers within Cachar 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_69e11e3c4c5c81908d336165816b12e0 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12a2e47a88190a3b5c05398605f68 |
completed | April 28, 2026, 9:44 p.m. |
Created at: April 16, 2026, 8:34 p.m.