Triple
T15040395
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Söderort |
E378584
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Rågsved
Rågsved is a suburban district in southern Stockholm, Sweden, known for its post-war residential architecture and metro station on the Green line.
|
E1132934
|
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: Rågsved | Statement: [Söderort, hasPart, Rågsved]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rågsved Context triple: [Söderort, hasPart, Rågsved]
-
A.
Rønne
Rønne is the largest town and administrative center of the Danish island of Bornholm, known for its historic harbor, half-timbered houses, and Baltic Sea ferry connections.
-
B.
Vildbjerg
Vildbjerg is a Danish town that serves as the administrative center of the former Trehøje Municipality in the Central Denmark Region.
-
C.
Nakskov
Nakskov is a historic port town in southern Denmark located on the island of Lolland, known for its maritime industry and coastal setting.
-
D.
Søllerød
Søllerød is a locality in Rudersdal Municipality, north of Copenhagen in eastern Denmark, known for its affluent residential areas and scenic natural surroundings.
-
E.
Fåvang
Fåvang is a village in Innlandet county, Norway, known for its rural setting in the Gudbrandsdalen valley and its historic stave church.
- 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: Rågsved Triple: [Söderort, hasPart, Rågsved]
Generated description
Rågsved is a suburban district in southern Stockholm, Sweden, known for its post-war residential architecture and metro station on the Green line.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Rågsved Target entity description: Rågsved is a suburban district in southern Stockholm, Sweden, known for its post-war residential architecture and metro station on the Green line.
-
A.
Rønne
Rønne is the largest town and administrative center of the Danish island of Bornholm, known for its historic harbor, half-timbered houses, and Baltic Sea ferry connections.
-
B.
Vildbjerg
Vildbjerg is a Danish town that serves as the administrative center of the former Trehøje Municipality in the Central Denmark Region.
-
C.
Nakskov
Nakskov is a historic port town in southern Denmark located on the island of Lolland, known for its maritime industry and coastal setting.
-
D.
Søllerød
Søllerød is a locality in Rudersdal Municipality, north of Copenhagen in eastern Denmark, known for its affluent residential areas and scenic natural surroundings.
-
E.
Fåvang
Fåvang is a village in Innlandet county, Norway, known for its rural setting in the Gudbrandsdalen valley and its historic stave church.
- 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_69ded82e79a481908ddb9609af8c4407 |
completed | April 15, 2026, 12:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe9de388508190bb0ecc04740cbe15 |
completed | May 9, 2026, 2:37 a.m. |
| NEDg | Description generation | batch_69fe9f2b71808190b961193ae1ddebf0 |
completed | May 9, 2026, 2:42 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe9fa89bd481909235d2ec377a0d8e |
completed | May 9, 2026, 2:44 a.m. |
Created at: April 10, 2026, 3 a.m.