Triple
T25688052
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bac Ninh |
E644116
|
entity |
| Predicate | distanceToHanoiApprox |
P188410
|
FINISHED |
| Object | about 30 km |
—
|
LITERAL 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: about 30 km | Statement: [Bac Ninh, distanceToHanoiApprox, about 30 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToHanoiApprox Context triple: [Bac Ninh, distanceToHanoiApprox, about 30 km]
-
A.
distanceToHeilbronn
Indicates the spatial distance between a given entity or location and the city of Heilbronn.
-
B.
distanceToPahiatua
Indicates the measured spatial distance between a given location and the place referred to as Pahiatua.
-
C.
approxDistanceToJos
Indicates that one entity is located at an approximate distance from the entity referred to as Jos.
-
D.
distanceToNapier
Indicates the spatial distance between a given entity and the location identified as Napier.
-
E.
distanceToTerminal
Indicates the measured or calculated distance from a given entity or location to a specified terminal point or facility.
- F. None of above. chosen
Provenance (4 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_69e77e8046888190b07ffa58c7e2c37a |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69fba78aca4c8190b8f1831e8cc04e06 |
completed | May 6, 2026, 8:41 p.m. |
| PD | Predicate disambiguation | batch_69fba34a65a4819088bac6c17542d71c |
completed | May 6, 2026, 8:23 p.m. |
| PDg | Predicate description generation | batch_69fba789c1188190973a919bfe2871f3 |
completed | May 6, 2026, 8:41 p.m. |
Created at: April 21, 2026, 8:12 p.m.