Triple
T22611646
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lørenskog municipality |
E566718
|
entity |
| Predicate | hasRailwayStation |
P918
|
FINISHED |
| Object |
Hanaborg Station
Hanaborg Station is a local railway stop serving the residential area of Hanaborg in Lørenskog, just east of Oslo, Norway.
|
E1546180
|
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: Hanaborg Station | Statement: [Lørenskog municipality, hasRailwayStation, Hanaborg Station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hanaborg Station Context triple: [Lørenskog municipality, hasRailwayStation, Hanaborg Station]
-
A.
Stange Station
Stange Station is a railway station serving the village of Stange in Innlandet county, Norway, providing regional and intercity train connections.
-
B.
Korgen Station
Korgen Station is a railway station serving the village of Korgen in the municipality of Hemnes in Nordland county, Norway.
-
C.
Bachman station
Bachman station is a public transit stop in Dallas, Texas, served by DART’s Green Line light rail system.
-
D.
Lunner Station
Lunner Station is a local railway station in Lunner, Norway, serving regional passenger traffic on the Gjøvik Line.
-
E.
Vestby Station
Vestby Station is a railway station in Vestby, Norway, serving as a stop on the Østfold Line for regional and commuter trains.
- 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: Hanaborg Station Triple: [Lørenskog municipality, hasRailwayStation, Hanaborg Station]
Generated description
Hanaborg Station is a local railway stop serving the residential area of Hanaborg in Lørenskog, just east of Oslo, Norway.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hanaborg Station Target entity description: Hanaborg Station is a local railway stop serving the residential area of Hanaborg in Lørenskog, just east of Oslo, Norway.
-
A.
Stange Station
Stange Station is a railway station serving the village of Stange in Innlandet county, Norway, providing regional and intercity train connections.
-
B.
Korgen Station
Korgen Station is a railway station serving the village of Korgen in the municipality of Hemnes in Nordland county, Norway.
-
C.
Bachman station
Bachman station is a public transit stop in Dallas, Texas, served by DART’s Green Line light rail system.
-
D.
Lunner Station
Lunner Station is a local railway station in Lunner, Norway, serving regional passenger traffic on the Gjøvik Line.
-
E.
Vestby Station
Vestby Station is a railway station in Vestby, Norway, serving as a stop on the Østfold Line for regional and commuter trains.
- 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_69e245884860819081046ce07d5872c4 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f167eb08c88190bf2380fa8575d2da |
completed | April 29, 2026, 2:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0b4dfb3bbc8190a80293adab818937 |
completed | May 18, 2026, 5:35 p.m. |
| NEDg | Description generation | batch_6a0b4f24dd9c81908f6907c75d4c6da7 |
completed | May 18, 2026, 5:40 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0b4fffea548190b0c3d9cce1c89aa9 |
completed | May 18, 2026, 5:44 p.m. |
Created at: April 17, 2026, 2:56 p.m.