Triple
T20235593
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vaxholm |
E498140
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Vaxholm town |
—
|
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: Vaxholm town | Statement: [Vaxholm, hasPart, Vaxholm town]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vaxholm town Context triple: [Vaxholm, hasPart, Vaxholm town]
-
A.
Vaxholm
chosen
Vaxholm is a small coastal town and municipality in the Stockholm archipelago of eastern Sweden, known for its historic fortress and picturesque waterfront.
-
B.
Vaxholm Municipality
Vaxholm Municipality is a coastal municipality in east-central Sweden known for its archipelago setting and historic fortress town of Vaxholm.
-
C.
Bruntinge
Bruntinge is a small village located in the municipality of Midden-Drenthe in the Dutch province of Drenthe.
-
D.
Hammarö
Hammarö is a Swedish island and municipality in Värmland County, known for its forests, coastline, and proximity to the city of Karlstad.
-
E.
Hässleholm
Hässleholm is a town in southern Sweden’s Skåne County known as a regional railway hub and service 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_69da6274c58c81909c646eabed6f4f30 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e6716995b88190a8514b41232b0e94 |
completed | April 20, 2026, 6:33 p.m. |
Created at: April 11, 2026, 11:40 p.m.