Triple
T11234702
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rangpur Division |
E265912
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object |
Thakurgaon
Thakurgaon is a northern district town in Bangladesh known for its agriculture-based economy and location near the Indian border.
|
E924049
|
NE FINISHED |
How this triple was built (4 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: Thakurgaon | Statement: [Rangpur Division, containsCity, Thakurgaon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Thakurgaon Context triple: [Rangpur Division, containsCity, Thakurgaon]
-
A.
Chapainawabganj
Chapainawabganj is a district town in western Bangladesh known for its mango production and location near the border with India.
-
B.
Jamalpur
Jamalpur is a city in central Bangladesh known as an important regional hub for agriculture and trade near the Jamuna River.
-
C.
Keraniganj
Keraniganj is a suburban upazila of Dhaka, Bangladesh, known for its dense population, river-based commerce, and numerous garment and brick industries.
-
D.
Lakhipur
Lakhipur is a notable town in the Indian state of Assam, recognized as one of the main urban centers within Cachar district.
-
E.
Pabna
Pabna is a town and district in present-day Bangladesh, historically part of British India's Bengal region and known for its role in agrarian movements and regional administration.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Thakurgaon Triple: [Rangpur Division, containsCity, Thakurgaon]
Generated description
Thakurgaon is a northern district town in Bangladesh known for its agriculture-based economy and location near the Indian border.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Thakurgaon Target entity description: Thakurgaon is a northern district town in Bangladesh known for its agriculture-based economy and location near the Indian border.
-
A.
Chapainawabganj
Chapainawabganj is a district town in western Bangladesh known for its mango production and location near the border with India.
-
B.
Jamalpur
Jamalpur is a city in central Bangladesh known as an important regional hub for agriculture and trade near the Jamuna River.
-
C.
Keraniganj
Keraniganj is a suburban upazila of Dhaka, Bangladesh, known for its dense population, river-based commerce, and numerous garment and brick industries.
-
D.
Lakhipur
Lakhipur is a notable town in the Indian state of Assam, recognized as one of the main urban centers within Cachar district.
-
E.
Pabna
Pabna is a town and district in present-day Bangladesh, historically part of British India's Bengal region and known for its role in agrarian movements and regional administration.
- F. None of above. chosen
Provenance (5 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_69d6aac656d48190b275efaa7d6074ee |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e903b8ec81909f9c89776d35c650 |
completed | April 9, 2026, 5:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e5b795f7948190a0dd53e8e034fe58 |
completed | April 20, 2026, 5:20 a.m. |
| NEDg | Description generation | batch_69e5bb5d6e0c8190933cd3d6e83c24a2 |
completed | April 20, 2026, 5:36 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e5c29d7b608190ae79bb8318211547 |
completed | April 20, 2026, 6:07 a.m. |
Created at: April 8, 2026, 9:30 p.m.