Triple
T18570571
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2014 Asia Cup |
E453862
|
entity |
| Predicate | hostCity |
P1798
|
FINISHED |
| Object | Dhaka |
—
|
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: Dhaka | Statement: [2014 Asia Cup, hostCity, Dhaka]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dhaka Context triple: [2014 Asia Cup, hostCity, Dhaka]
-
A.
Dhaka
chosen
Dhaka is the capital and largest city of Bangladesh, serving as the country’s political, economic, and cultural center.
-
B.
Dhaka
Dhaka is a town in the East Champaran district of Bihar, India, known as a local administrative and commercial center in the region.
-
C.
Chittagong
Chittagong is a major coastal city and Bangladesh’s principal seaport, known for its bustling maritime trade and industrial significance.
-
D.
Rangpur
Rangpur is a city in northern Bangladesh known as a regional administrative, cultural, and commercial center.
-
E.
Greater Dhaka
Greater Dhaka is the densely populated metropolitan region centered on Bangladesh’s capital city, Dhaka, encompassing its urban core and surrounding suburban and peri-urban areas.
- 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_69d8d38974308190a9174430ef256b73 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e53b01331c8190bec3aba40358a843 |
completed | April 19, 2026, 8:28 p.m. |
Created at: April 10, 2026, 11:43 a.m.