Triple
T9886346
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Khulna Division |
E180941
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | Meherpur |
E676483
|
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: Meherpur | Statement: [Khulna Division, containsCity, Meherpur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Meherpur Context triple: [Khulna Division, containsCity, Meherpur]
-
A.
Jamalpur
Jamalpur is a city in central Bangladesh known as an important regional hub for agriculture and trade near the Jamuna River.
-
B.
Meherpur District
chosen
Meherpur District is a small administrative region in southwestern Bangladesh known for its historical significance in the country’s independence movement.
-
C.
Lakhipur
Lakhipur is a notable town in the Indian state of Assam, recognized as one of the main urban centers within Cachar district.
-
D.
Sandeshkhali
Sandeshkhali is a rural town and community development block in the North 24 Parganas district of West Bengal, India, known for its riverine landscape and proximity to the Sundarbans region.
-
E.
Jahangirpuri
Jahangirpuri is a residential and commercial neighborhood in North West Delhi, India, known for its dense urban character and connectivity via the Delhi Metro.
- 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_69ca828082cc8190a40f8d299caa6545 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cdb45659748190a3ebd1abe23c8779 |
completed | April 2, 2026, 12:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1eafc76b0819098e481bb17a3af2b |
completed | April 5, 2026, 4:54 a.m. |
Created at: March 30, 2026, 8:38 p.m.