Triple
T20904635
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Manikganj District |
E514760
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object | Manikganj town |
—
|
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: Manikganj town | Statement: [Manikganj District, hasSettlement, Manikganj town]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Manikganj town Context triple: [Manikganj District, hasSettlement, Manikganj town]
-
A.
Manikganj
chosen
Manikganj is a town in central Bangladesh that serves as the administrative and commercial hub of Manikganj District.
-
B.
Begumganj
Begumganj is a town and administrative subdivision in the Raisen district of Madhya Pradesh, India.
-
C.
Jamalpur
Jamalpur is a city in central Bangladesh known as an important regional hub for agriculture and trade near the Jamuna River.
-
D.
Liaquatabad Town
Liaquatabad Town is a densely populated residential and commercial locality in Karachi, Pakistan, known for its bustling markets and central urban location.
-
E.
Keraniganj
Keraniganj is a suburban upazila of Dhaka, Bangladesh, known for its dense population, river-based commerce, and numerous garment and brick industries.
- 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_69e0b4f8a1108190bce3d31331290ced |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6e8ff36488190987ecdfcbed4220c |
completed | April 21, 2026, 3:03 a.m. |
Created at: April 16, 2026, 12:47 p.m.