Triple
T10687875
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Östergötland County |
E251928
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object |
Finspång
Finspång is a small industrial town in eastern Sweden known for its long history of metalworking and turbine manufacturing.
|
E879115
|
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: Finspång | Statement: [Östergötland County, containsCity, Finspång]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Finspång Context triple: [Östergötland County, containsCity, Finspång]
-
A.
Strängnäs
Strängnäs is a historic Swedish town known for its medieval cathedral and picturesque location on the shores of Lake Mälaren.
-
B.
Nässjö
Nässjö is a small Swedish town in Jönköping County known as a regional railway hub and service center in southern Sweden.
-
C.
Falköping
Falköping is a small Swedish town known for its surrounding ancient burial mounds, rolling agricultural landscape, and location between the plateaus of Mösseberg and Ålleberg.
-
D.
Sandviken
Sandviken is an industrial town in central Sweden, best known as the historic home of the steel company Sandvik.
-
E.
Ronneby
Ronneby is a historic town in southern Sweden known for its well-preserved wooden architecture, spa traditions, and scenic location in Blekinge County.
- 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: Finspång Triple: [Östergötland County, containsCity, Finspång]
Generated description
Finspång is a small industrial town in eastern Sweden known for its long history of metalworking and turbine manufacturing.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Finspång Target entity description: Finspång is a small industrial town in eastern Sweden known for its long history of metalworking and turbine manufacturing.
-
A.
Strängnäs
Strängnäs is a historic Swedish town known for its medieval cathedral and picturesque location on the shores of Lake Mälaren.
-
B.
Nässjö
Nässjö is a small Swedish town in Jönköping County known as a regional railway hub and service center in southern Sweden.
-
C.
Falköping
Falköping is a small Swedish town known for its surrounding ancient burial mounds, rolling agricultural landscape, and location between the plateaus of Mösseberg and Ålleberg.
-
D.
Sandviken
Sandviken is an industrial town in central Sweden, best known as the historic home of the steel company Sandvik.
-
E.
Ronneby
Ronneby is a historic town in southern Sweden known for its well-preserved wooden architecture, spa traditions, and scenic location in Blekinge County.
- 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_69d6aa5bd7c08190a816e733b4045c23 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6fd19f0f481909eeaa75d17d9c060 |
completed | April 9, 2026, 1:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d9889d1f988190938be54771161b00 |
completed | April 10, 2026, 11:32 p.m. |
| NEDg | Description generation | batch_69d98aeb82988190a17b009c74279423 |
completed | April 10, 2026, 11:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d98c2aae048190b348e5614ff23f03 |
completed | April 10, 2026, 11:47 p.m. |
Created at: April 8, 2026, 9:11 p.m.