Triple
T6302554
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dhaka Division |
E141290
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Gazipur City |
E591401
|
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: Gazipur City | Statement: [Dhaka Division, contains, Gazipur City]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gazipur City Context triple: [Dhaka Division, contains, Gazipur City]
-
A.
Gazipur District
chosen
Gazipur District is an important industrial and suburban area of central Bangladesh known for its garment factories, educational institutions, and proximity to the capital, Dhaka.
-
B.
Narayanganj City
Narayanganj City is a major industrial and river port city in central Bangladesh, known for its textile and jute industries and its proximity to the capital, Dhaka.
-
C.
Sirajganj
Sirajganj is a city in north-central Bangladesh known as a key river port and commercial hub on the banks of the Jamuna River.
-
D.
Narayanganj District
Narayanganj District is an industrially important and densely populated district in central Bangladesh, known especially for its textile and jute industries and its proximity to the capital, Dhaka.
-
E.
Liaquatabad Town
Liaquatabad Town is a densely populated residential and commercial locality in Karachi, Pakistan, known for its bustling markets and central urban location.
- 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_69c008cf0ad4819095def81e2bd42f9f |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0645cfca88190ace060ef5b0e00e8 |
completed | March 22, 2026, 9:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c64ba5b0bc8190aefa07c77c99be83 |
completed | March 27, 2026, 9:19 a.m. |
Created at: March 22, 2026, 4:27 p.m.