Triple
T7640105
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rajshahi Railway Station |
E172977
|
entity |
| Predicate | connectsTo |
P845
|
FINISHED |
| Object | Sirajganj |
E264217
|
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: Sirajganj | Statement: [Rajshahi Railway Station, connectsTo, Sirajganj]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sirajganj Context triple: [Rajshahi Railway Station, connectsTo, Sirajganj]
-
A.
Sirajganj
chosen
Sirajganj is a city in north-central Bangladesh known as a key river port and commercial hub on the banks of the Jamuna River.
-
B.
Faridpur
Faridpur is a historic town in central Bangladesh known for its cultural heritage and role in the Bengal Renaissance.
-
C.
Jamalpur
Jamalpur is a city in central Bangladesh known as an important regional hub for agriculture and trade near the Jamuna River.
-
D.
Chapainawabganj
Chapainawabganj is a district town in western Bangladesh known for its mango production and location near the border with India.
-
E.
Rangpur
Rangpur is a city in northern Bangladesh known as a regional administrative, cultural, and commercial center.
- 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_69c6995360188190968ee57b72a1627f |
completed | March 27, 2026, 2:50 p.m. |
| NER | Named-entity recognition | batch_69c6facc4b5481908697e662b0991e3f |
completed | March 27, 2026, 9:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8be1db7548190a6a6d280922a195d |
completed | March 29, 2026, 5:52 a.m. |
Created at: March 27, 2026, 3:57 p.m.