Triple
T2249489
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of Dhaka |
E49583
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Dhaka |
E26021
|
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: Dhaka | Statement: [University of Dhaka, locatedIn, Dhaka]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dhaka Context triple: [University of Dhaka, locatedIn, Dhaka]
-
A.
Dhaka
chosen
Dhaka is the capital and largest city of Bangladesh, serving as the country’s political, economic, and cultural center.
-
B.
Chittagong
Chittagong is a major coastal city and Bangladesh’s principal seaport, known for its bustling maritime trade and industrial significance.
-
C.
Dhaka District
Dhaka District is an administrative region in central Bangladesh that encompasses the nation’s capital city and serves as a major political, economic, and cultural hub.
-
D.
Rajshahi
Rajshahi is a prominent city in western Bangladesh, known as an important cultural, educational, and commercial center of the Bengal region.
-
E.
Barisal
Barisal is a major city in southern Bangladesh, historically known as a cultural and riverine hub of the Bengal region.
- 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_69a88aa979788190ad6500f1d8eee2fc |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc0ef74988190a0af51d983cf5658 |
completed | March 7, 2026, 6:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae6b1948cc8190921b9fcc12c28db0 |
completed | March 9, 2026, 6:39 a.m. |
Created at: March 4, 2026, 7:47 p.m.