Triple
T14126065
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Närke |
E340035
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object |
Hallsberg
Hallsberg is a Swedish railway town in Örebro County known as a major junction in the national rail network.
|
E1081312
|
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: Hallsberg | Statement: [Närke, containsTown, Hallsberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hallsberg Context triple: [Närke, containsTown, Hallsberg]
-
A.
Hesselberg
Hesselberg is a prominent hill in Bavaria, Germany, known as the highest elevation of the Franconian Alb region.
-
B.
Rosersberg
Rosersberg is a locality in Stockholm County, Sweden, known for its historic Rosersberg Palace and its location near Stockholm Arlanda Airport.
-
C.
Flesberg
Flesberg is a rural municipality in southeastern Norway known for its forests, traditional wooden architecture, and location in the Numedal valley.
-
D.
Norsborg
Norsborg is a suburban district in Botkyrka Municipality, southwest of central Stockholm, Sweden, known as the terminus area of the Stockholm metro’s red line.
-
E.
Hallonbergen
Hallonbergen is a residential district and metro-served suburb in the Stockholm urban area, known for its 1960s–70s apartment blocks and multicultural population.
- 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: Hallsberg Triple: [Närke, containsTown, Hallsberg]
Generated description
Hallsberg is a Swedish railway town in Örebro County known as a major junction in the national rail network.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hallsberg Target entity description: Hallsberg is a Swedish railway town in Örebro County known as a major junction in the national rail network.
-
A.
Hesselberg
Hesselberg is a prominent hill in Bavaria, Germany, known as the highest elevation of the Franconian Alb region.
-
B.
Rosersberg
Rosersberg is a locality in Stockholm County, Sweden, known for its historic Rosersberg Palace and its location near Stockholm Arlanda Airport.
-
C.
Flesberg
Flesberg is a rural municipality in southeastern Norway known for its forests, traditional wooden architecture, and location in the Numedal valley.
-
D.
Norsborg
Norsborg is a suburban district in Botkyrka Municipality, southwest of central Stockholm, Sweden, known as the terminus area of the Stockholm metro’s red line.
-
E.
Hallonbergen
Hallonbergen is a residential district and metro-served suburb in the Stockholm urban area, known for its 1960s–70s apartment blocks and multicultural population.
- 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_69d81c6a95b481909e39111e0c1f31ee |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de6096976481909dc79066c5165a50 |
completed | April 14, 2026, 3:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcdf0c833081908458e4eaee689df7 |
completed | May 7, 2026, 6:50 p.m. |
| NEDg | Description generation | batch_69fce094bf3081909f7c0097dcb63398 |
completed | May 7, 2026, 6:57 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fce14ff8e48190b3b663d130d18418 |
completed | May 7, 2026, 7 p.m. |
Created at: April 9, 2026, 10:22 p.m.