Triple
T14372309
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Songdo International Business District |
E356385
|
entity |
| Predicate | distanceToIncheonInternationalAirport |
P79745
|
FINISHED |
| Object | about 15 kilometres |
—
|
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 15 kilometres | Statement: [Songdo International Business District, distanceToIncheonInternationalAirport, about 15 kilometres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToIncheonInternationalAirport Context triple: [Songdo International Business District, distanceToIncheonInternationalAirport, about 15 kilometres]
-
A.
distanceToSeoul
Indicates the measured or estimated spatial distance between a given entity’s location and the city of Seoul.
-
B.
distanceToAirport
chosen
Indicates the measured distance between a given location and the nearest or specified airport.
-
C.
distanceFromKochi_km
Indicates the physical distance, measured in kilometers, between a given location and Kochi.
-
D.
distanceToShinjukuStation_km
Indicates the physical distance, measured in kilometers, between a given place and Shinjuku Station.
-
E.
distanceToKoreanPeninsula
Indicates the measured or estimated spatial distance between a given entity or location and the Korean Peninsula.
- 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_69d8279163a081908aec45c0e3f1e02f |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de8fb2082c8190b42cc5f2bab4f574 |
completed | April 14, 2026, 7:04 p.m. |
| PD | Predicate disambiguation | batch_69de2a9cb3e081909f6b33fdd939bb9e |
completed | April 14, 2026, 11:53 a.m. |
Created at: April 10, 2026, 1:15 a.m.