Triple
T17189704
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | National Martyrs’ Memorial |
E417194
|
entity |
| Predicate | nearbyCity |
P350
|
FINISHED |
| Object | Savar |
E599203
|
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: Savar | Statement: [National Martyrs’ Memorial, nearbyCity, Savar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Savar Context triple: [National Martyrs’ Memorial, nearbyCity, Savar]
-
A.
Savar
chosen
Savar is a suburban area near Dhaka in Bangladesh, known for its educational institutions, industrial zones, and historical significance.
-
B.
Rajshahi
Rajshahi is a prominent city in western Bangladesh, known as an important cultural, educational, and commercial center of the Bengal region.
-
C.
Bileh Savar
Bileh Savar is a city in northwestern Iran known as a border town near Azerbaijan in Ardabil Province.
-
D.
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.
-
E.
Rangpur
Rangpur is a city in northern Bangladesh known as a regional administrative, cultural, and commercial center.
- 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_69d886d6ba8c819093215917b3d01689 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e42d986b60819085c515101cfe65fe |
completed | April 19, 2026, 1:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0180c85b148190825bc99b28363c89 |
completed | May 11, 2026, 7:10 a.m. |
Created at: April 10, 2026, 5:37 a.m.