Triple
T25084781
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daejeon-yeok |
E628283
|
entity |
| Predicate | distanceFromSeoulStation |
P69004
|
FINISHED |
| Object | approximately 166 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: approximately 166 km | Statement: [Daejeon-yeok, distanceFromSeoulStation, approximately 166 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromSeoulStation Context triple: [Daejeon-yeok, distanceFromSeoulStation, approximately 166 km]
-
A.
distanceToSeoul
Indicates the measured or estimated spatial distance between a given entity’s location and the city of Seoul.
-
B.
distanceFromGwangju
Indicates the spatial distance between a given location and the city of Gwangju.
-
C.
distanceFromApia
Indicates the measured distance between a given location and Apia.
-
D.
distanceToShinjukuStation_km
Indicates the physical distance, measured in kilometers, between a given place and Shinjuku Station.
-
E.
distanceFromStation
chosen
Indicates the measured spatial separation between an entity and a specified 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_69e2ff2e73f881909992bf3eda5c25cb |
completed | April 18, 2026, 3:49 a.m. |
| NER | Named-entity recognition | batch_69f6c1265c208190aacd2b551f8f0f82 |
completed | May 3, 2026, 3:29 a.m. |
| PD | Predicate disambiguation | batch_69f6bd2415fc81908c23c311aebce66f |
completed | May 3, 2026, 3:12 a.m. |
Created at: April 18, 2026, 6:23 a.m.