Triple
T18113034
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gran |
E433528
|
entity |
| Predicate | hasRailwayStation |
P918
|
FINISHED |
| Object |
Bleiken Station
Bleiken Station is a local railway stop serving the village of Bleiken in the municipality of Gran, Norway.
|
E1309538
|
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: Bleiken Station | Statement: [Gran, hasRailwayStation, Bleiken Station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bleiken Station Context triple: [Gran, hasRailwayStation, Bleiken Station]
-
A.
Hengst station
Hengst station is a mountain railway terminus serving as the upper endpoint of the Schneeberg line in Lower Austria.
-
B.
Singen station
Singen station is a major railway hub in southern Germany that serves as an important interchange point for regional and long-distance train services near the Swiss border.
-
C.
Beynes station
Beynes station is a suburban railway station in Beynes, France, served by Paris’ Transilien commuter network.
-
D.
Wende station
Wende station is a metro station on Taipei's Wenhu (Brown) Line serving the Neihu District in Taiwan.
-
E.
Kampen station
Kampen station is a railway station in the city of Kampen in the Netherlands, serving as a local stop on the regional rail 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: Bleiken Station Triple: [Gran, hasRailwayStation, Bleiken Station]
Generated description
Bleiken Station is a local railway stop serving the village of Bleiken in the municipality of Gran, Norway.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bleiken Station Target entity description: Bleiken Station is a local railway stop serving the village of Bleiken in the municipality of Gran, Norway.
-
A.
Hengst station
Hengst station is a mountain railway terminus serving as the upper endpoint of the Schneeberg line in Lower Austria.
-
B.
Singen station
Singen station is a major railway hub in southern Germany that serves as an important interchange point for regional and long-distance train services near the Swiss border.
-
C.
Beynes station
Beynes station is a suburban railway station in Beynes, France, served by Paris’ Transilien commuter network.
-
D.
Wende station
Wende station is a metro station on Taipei's Wenhu (Brown) Line serving the Neihu District in Taiwan.
-
E.
Kampen station
Kampen station is a railway station in the city of Kampen in the Netherlands, serving as a local stop on the regional rail 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_69d8b90916008190a1f110bd7ced5473 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4ddd3fd9c81909bfe95927f7553e3 |
completed | April 19, 2026, 1:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a038fadf97c8190b3c62e9ca89dc0cb |
completed | May 12, 2026, 8:38 p.m. |
| NEDg | Description generation | batch_6a0391a92bd88190ba2bc79471a3ab55 |
completed | May 12, 2026, 8:46 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a03922a2a148190917adab265da0e55 |
completed | May 12, 2026, 8:48 p.m. |
Created at: April 10, 2026, 10:28 a.m.