Triple
T20498141
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kaesong |
E503225
|
entity |
| Predicate | distanceToSeoulApproxKm |
P48419
|
FINISHED |
| Object | about 70 |
—
|
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 70 | Statement: [Kaesong, distanceToSeoulApproxKm, about 70]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToSeoulApproxKm Context triple: [Kaesong, distanceToSeoulApproxKm, about 70]
-
A.
distanceToSeoul
chosen
Indicates the measured or estimated spatial distance between a given entity’s location and the city of Seoul.
-
B.
distanceToKoreanPeninsula
Indicates the measured or estimated spatial distance between a given entity or location and the Korean Peninsula.
-
C.
directionFromSeoul
Indicates the cardinal or relative compass direction in which an entity is located when measured from Seoul.
-
D.
distanceFromKochi_km
Indicates the physical distance, measured in kilometers, between a given location and Kochi.
-
E.
distanceToShinjukuStation_km
Indicates the physical distance, measured in kilometers, between a given place and Shinjuku Station.
- 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_69e0b4b1e52c8190894281cf7e3283ab |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e69cbefe4c819098af5bfd4d92341d |
completed | April 20, 2026, 9:38 p.m. |
| PD | Predicate disambiguation | batch_69e59fcdf6e08190a604204615dc56e6 |
completed | April 20, 2026, 3:38 a.m. |
Created at: April 16, 2026, 11:35 a.m.