Triple
T1300154
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Södermanland County |
E27742
|
entity |
| Predicate | hasUrbanArea |
P316
|
FINISHED |
| Object | Strängnäs |
E176028
|
NE FINISHED |
How this triple was built (2 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: Strängnäs | Statement: [Södermanland County, hasUrbanArea, Strängnäs]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Strängnäs Context triple: [Södermanland County, hasUrbanArea, Strängnäs]
-
A.
Strängnäs
chosen
Strängnäs is a historic Swedish town known for its medieval cathedral and picturesque location on the shores of Lake Mälaren.
-
B.
Strömstad
Strömstad is a coastal town and municipality in western Sweden, near the Norwegian border, known for its archipelago, tourism, and ferry connections.
-
C.
Bollstanäs
Bollstanäs is a residential locality in Sweden situated within the suburban area of Upplands Väsby, north of Stockholm.
-
D.
Bollnäs
Bollnäs is a small Swedish town known for its scenic lakeside setting, traditional wooden architecture, and strong bandy sports culture.
-
E.
Eskilstuna
Eskilstuna is an industrial city in central Sweden known historically for its metalworking and engineering industries.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69a496d6682881909ba658f1c1e0e2b0 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c11314a48190ab4efb8b1acdce50 |
completed | March 1, 2026, 10:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adeab797c48190a3a2f7313638f594 |
completed | March 8, 2026, 9:31 p.m. |
Created at: March 1, 2026, 7:51 p.m.