Triple
T15030184
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | T-bana |
E378321
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Skarpnäck
Skarpnäck is a residential district in southern Stockholm, Sweden, known for its postwar housing areas and as the terminus of the green line on the Stockholm metro.
|
E1134017
|
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: Skarpnäck | Statement: [T-bana, hasStation, Skarpnäck]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Skarpnäck Context triple: [T-bana, hasStation, Skarpnäck]
-
A.
Skarpäng
Skarpäng is a residential urban area within Täby Municipality in Stockholm County, Sweden.
-
B.
Mariaberget
Mariaberget is a historic, picturesque area on the western side of Södermalm in central Stockholm, known for its well-preserved old buildings and panoramic views over the city and Lake Mälaren.
-
C.
Malmberget
Malmberget is a major iron ore mining town in northern Sweden known for its extensive underground operations and associated subsidence issues.
-
D.
Gustavsberg
Gustavsberg is a locality in Sweden best known for its historic porcelain factory and role as a suburban community in the Stockholm archipelago.
-
E.
Gundeberga
Gundeberga was a 7th-century Lombard queen consort of Italy, known as the wife of King Rothari.
- 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: Skarpnäck Triple: [T-bana, hasStation, Skarpnäck]
Generated description
Skarpnäck is a residential district in southern Stockholm, Sweden, known for its postwar housing areas and as the terminus of the green line on the Stockholm metro.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Skarpnäck Target entity description: Skarpnäck is a residential district in southern Stockholm, Sweden, known for its postwar housing areas and as the terminus of the green line on the Stockholm metro.
-
A.
Skarpäng
Skarpäng is a residential urban area within Täby Municipality in Stockholm County, Sweden.
-
B.
Mariaberget
Mariaberget is a historic, picturesque area on the western side of Södermalm in central Stockholm, known for its well-preserved old buildings and panoramic views over the city and Lake Mälaren.
-
C.
Malmberget
Malmberget is a major iron ore mining town in northern Sweden known for its extensive underground operations and associated subsidence issues.
-
D.
Gustavsberg
Gustavsberg is a locality in Sweden best known for its historic porcelain factory and role as a suburban community in the Stockholm archipelago.
-
E.
Gundeberga
Gundeberga was a 7th-century Lombard queen consort of Italy, known as the wife of King Rothari.
- 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_69d85cd46b2c819090d054c27787f677 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded7e2416081908dfba48d7f7b4a84 |
completed | April 15, 2026, 12:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe9dd967588190821cf47e9734db21 |
completed | May 9, 2026, 2:37 a.m. |
| NEDg | Description generation | batch_69fe9e5dbbe0819084567688758b0245 |
completed | May 9, 2026, 2:39 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe9eedca1481908ce438991184d62e |
completed | May 9, 2026, 2:41 a.m. |
Created at: April 10, 2026, 2:59 a.m.