Triple
T27524026
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Japan–South Korea |
E694786
|
entity |
| Predicate | hasAirRoute |
P105188
|
FINISHED |
| Object |
Osaka–Seoul
Osaka–Seoul is a heavily traveled international air route linking Japan’s Kansai region with the capital of South Korea for business and tourism.
|
E1779541
|
NE FINISHED |
How this triple was built (3 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: Osaka–Seoul | Statement: [Japan–South Korea, hasAirRoute, Osaka–Seoul]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Osaka–Seoul Triple: [Japan–South Korea, hasAirRoute, Osaka–Seoul]
Generated description
Osaka–Seoul is a heavily traveled international air route linking Japan’s Kansai region with the capital of South Korea for business and tourism.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAirRoute Context triple: [Japan–South Korea, hasAirRoute, Osaka–Seoul]
-
A.
hasAirTravelLink
chosen
Indicates that there is a direct or established air travel connection (such as flights or air routes) between the related entities.
-
B.
hasAirlines
Indicates that one entity (such as an airport, city, or country) is served by or associated with one or more airline operators.
-
C.
hasAirportAccessTo
Indicates that one location or entity has direct access to another via an airport connection or service.
-
D.
hasCityPair
Indicates a relationship that links two cities considered as a connected or associated pair, often for purposes such as travel, trade, or comparison.
-
E.
airlineServiceVia
Indicates that an airline service operates between two locations with a specified intermediate stop or transit point.
- F. None of above.
Provenance (6 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_69ef538550208190aa9de8e2cb260d93 |
completed | April 27, 2026, 12:16 p.m. |
| NER | Named-entity recognition | batch_69f62f2e2df88190948551f6fd67a65a |
completed | May 2, 2026, 5:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a12d0c4dbd081908975e065483e3bdb |
completed | May 24, 2026, 10:19 a.m. |
| NEDg | Description generation | batch_6a12d169e8888190bf3c8e7f0718a3a5 |
completed | May 24, 2026, 10:22 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a12d270e0dc81909c04761a32c1e652 |
completed | May 24, 2026, 10:26 a.m. |
| PD | Predicate disambiguation | batch_69f62c1762f881908c25e8f70ecd5041 |
completed | May 2, 2026, 4:53 p.m. |
Created at: April 27, 2026, 1:22 p.m.