Triple
T20798293
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trønderbanen |
E511970
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Leangen
Leangen is a railway station in Trondheim, Norway, serving local and regional trains on the Trønderbanen line.
|
E1452977
|
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: Leangen | Statement: [Trønderbanen, hasStation, Leangen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Leangen Context triple: [Trønderbanen, hasStation, Leangen]
-
A.
Langenes
Langenes is a small coastal village in the former Vågsøy municipality in Vestland county, western Norway.
-
B.
Lengnau
Lengnau is a Swiss municipality located in the canton of Solothurn, known for its residential character and proximity to the Jura mountains.
-
C.
Salangen
Salangen is a coastal municipality in Troms county in northern Norway, known for its fjords, fishing traditions, and the administrative center village of Sjøvegan.
-
D.
Langesund
Langesund is a coastal town in southern Norway known historically as a shipping and timber port and now as a popular summer and ferry destination.
-
E.
Lohberg
Lohberg is a small Bavarian village in the Bavarian Forest region of Germany, known as a gateway to outdoor activities around the Großer Arber mountain.
- 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: Leangen Triple: [Trønderbanen, hasStation, Leangen]
Generated description
Leangen is a railway station in Trondheim, Norway, serving local and regional trains on the Trønderbanen line.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Leangen Target entity description: Leangen is a railway station in Trondheim, Norway, serving local and regional trains on the Trønderbanen line.
-
A.
Langenes
Langenes is a small coastal village in the former Vågsøy municipality in Vestland county, western Norway.
-
B.
Lengnau
Lengnau is a Swiss municipality located in the canton of Solothurn, known for its residential character and proximity to the Jura mountains.
-
C.
Salangen
Salangen is a coastal municipality in Troms county in northern Norway, known for its fjords, fishing traditions, and the administrative center village of Sjøvegan.
-
D.
Langesund
Langesund is a coastal town in southern Norway known historically as a shipping and timber port and now as a popular summer and ferry destination.
-
E.
Lohberg
Lohberg is a small Bavarian village in the Bavarian Forest region of Germany, known as a gateway to outdoor activities around the Großer Arber mountain.
- 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_69e0b4cc69f481908e98751e697b9df4 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c2ae2c4c819087f620df31dc1aba |
completed | April 21, 2026, 12:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a090069bee8819086d6735f4f8b94c0 |
completed | May 16, 2026, 11:40 p.m. |
| NEDg | Description generation | batch_6a09017ffbc88190b9fe259ae773ea4f |
completed | May 16, 2026, 11:45 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0901ed76bc8190815c656c38f13626 |
completed | May 16, 2026, 11:46 p.m. |
Created at: April 16, 2026, 12:39 p.m.