Triple
T7671363
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rajshahi Medical College |
E173754
|
entity |
| Predicate | city |
P40
|
FINISHED |
| Object | Rajshahi |
E32151
|
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: Rajshahi | Statement: [Rajshahi Medical College, city, Rajshahi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rajshahi Context triple: [Rajshahi Medical College, city, Rajshahi]
-
A.
Rajshahi
chosen
Rajshahi is a prominent city in western Bangladesh, known as an important cultural, educational, and commercial center of the Bengal region.
-
B.
Barisal
Barisal is a major city in southern Bangladesh, historically known as a cultural and riverine hub of the Bengal region.
-
C.
Rangpur
Rangpur is a city in northern Bangladesh known as a regional administrative, cultural, and commercial center.
-
D.
Savar
Savar is a suburban area near Dhaka in Bangladesh, known for its educational institutions, industrial zones, and historical significance.
-
E.
Chittagong
Chittagong is a major coastal city and Bangladesh’s principal seaport, known for its bustling maritime trade and industrial significance.
- 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_69c699562484819086752091e3164a27 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c701dd3c808190990e07ced94b3297 |
completed | March 27, 2026, 10:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c9cbcc02fc8190b0f9049dffc22021 |
completed | March 30, 2026, 1:03 a.m. |
Created at: March 27, 2026, 4 p.m.