Triple
T2321303
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Askim |
E51185
|
entity |
| Predicate | hasRailwayStation |
P918
|
FINISHED |
| Object |
Askim Station
Askim Station is a railway station serving the town of Askim in Viken county, Norway, on the Eastern Østfold Line.
|
E256181
|
NE FINISHED |
How this triple was built (4 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: Askim Station | Statement: [Askim, hasRailwayStation, Askim Station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Askim Station Context triple: [Askim, hasRailwayStation, Askim Station]
-
A.
Sajik Station
Sajik Station is a subway station in Busan, South Korea, serving the Dongnae District on the Busan Metro network.
-
B.
Sasang Station
Sasang Station is a major railway and subway interchange in Busan, South Korea, serving as a key transit hub for both local and intercity travel.
-
C.
Nampo Station
Nampo Station is a major subway station and commercial hub in central Busan, South Korea, known for its proximity to popular shopping streets and tourist attractions.
-
D.
Beomeosa Station
Beomeosa Station is a subway station in Busan, South Korea, serving as a key access point to the nearby Beomeosa Temple and surrounding Geumjeong District area.
-
E.
Chebei South Station
Chebei South Station is a metro station in Guangzhou, China, serving passengers on the Guangzhou Metro network.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Askim Station Triple: [Askim, hasRailwayStation, Askim Station]
Generated description
Askim Station is a railway station serving the town of Askim in Viken county, Norway, on the Eastern Østfold Line.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Askim Station Target entity description: Askim Station is a railway station serving the town of Askim in Viken county, Norway, on the Eastern Østfold Line.
-
A.
Sajik Station
Sajik Station is a subway station in Busan, South Korea, serving the Dongnae District on the Busan Metro network.
-
B.
Sasang Station
Sasang Station is a major railway and subway interchange in Busan, South Korea, serving as a key transit hub for both local and intercity travel.
-
C.
Nampo Station
Nampo Station is a major subway station and commercial hub in central Busan, South Korea, known for its proximity to popular shopping streets and tourist attractions.
-
D.
Beomeosa Station
Beomeosa Station is a subway station in Busan, South Korea, serving as a key access point to the nearby Beomeosa Temple and surrounding Geumjeong District area.
-
E.
Chebei South Station
Chebei South Station is a metro station in Guangzhou, China, serving passengers on the Guangzhou Metro network.
- F. None of above. chosen
Provenance (5 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_69a88b074b908190ae983dbca7757d88 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc6337e948190bb4860f7045914e1 |
completed | March 7, 2026, 6:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae896911908190b53954dbf854cc18 |
completed | March 9, 2026, 8:48 a.m. |
| NEDg | Description generation | batch_69ae8d2dcc8081908d4274b2287ff2b8 |
completed | March 9, 2026, 9:04 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae8d786a648190acf0a14e0d4a120c |
completed | March 9, 2026, 9:06 a.m. |
Created at: March 4, 2026, 7:49 p.m.