Triple
T19734701
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nynäshamn Station |
E473946
|
entity |
| Predicate | serves |
P98
|
FINISHED |
| Object | Nynäshamn |
—
|
NE NERFINISHED |
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: Nynäshamn | Statement: [Nynäshamn Station, serves, Nynäshamn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nynäshamn Context triple: [Nynäshamn Station, serves, Nynäshamn]
-
A.
Nynäshamn
chosen
Nynäshamn is a coastal town in Stockholm County, Sweden, known for its ferry connections to Gotland and the Baltic states as well as its scenic archipelago setting.
-
B.
Söderhamn
Söderhamn is a coastal town in east-central Sweden known for its historical wooden architecture and role as the administrative and commercial center of the surrounding region.
-
C.
Fredrikshamn
Fredrikshamn (Hamina) is a coastal town in southeastern Finland that historically served as an important military and trading center.
-
D.
Skärhamn
Skärhamn is a coastal town in western Sweden known for its fishing heritage, picturesque harbor, and the Nordic Watercolour Museum.
-
E.
Kristinehamn
Kristinehamn is a small Swedish town in Värmland County known for its lakeside location on Vänern and its historical role as a regional trading and industrial center.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e517ebd48190979ee76723bcfadf |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6515d138c8190a4c4d112ed5756a3 |
completed | April 20, 2026, 4:16 p.m. |
Created at: April 10, 2026, 1:47 p.m.