Triple
T25732595
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ghojadanga |
E645282
|
entity |
| Predicate | borderCountryOnOtherSide |
P126842
|
FINISHED |
| Object | Bangladesh |
—
|
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: Bangladesh | Statement: [Ghojadanga, borderCountryOnOtherSide, Bangladesh]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: borderCountryOnOtherSide Context triple: [Ghojadanga, borderCountryOnOtherSide, Bangladesh]
-
A.
borderingCountryOnOtherSide
Indicates that one country lies on the opposite side of a shared border relative to another country.
-
B.
borderCountrySide
Indicates that one country shares a land border with the side or region of another country.
-
C.
hasBorderCrossingSide
Indicates that one side of a border crossing is associated with or located on a particular boundary or segment of that crossing.
-
D.
borderTownAcrossBorder
Indicates that a town lies on one side of a border directly opposite or adjacent to a town on the other side of that border.
-
E.
connectsToCountryBorder
chosen
Indicates that one entity is directly adjacent to and touches the border of a specified country.
- F. None of above.
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_69e77e85254081908d79ee4e8715f283 |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f67257b0448190a13011af81c81449 |
completed | May 2, 2026, 9:53 p.m. |
| PD | Predicate disambiguation | batch_69f66ec3d3d48190ab2f2b71939e572e |
completed | May 2, 2026, 9:38 p.m. |
Created at: April 21, 2026, 11:18 p.m.