Triple
T21129331
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ängelholm Flight Museum |
E520641
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object | Ängelholm |
—
|
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: Ängelholm | Statement: [Ängelholm Flight Museum, namedAfter, Ängelholm]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ängelholm Context triple: [Ängelholm Flight Museum, namedAfter, Ängelholm]
-
A.
Ängelholm
chosen
Ängelholm is a coastal town in southern Sweden known for its sandy beaches, aviation museum, and scenic location at the mouth of the Rönne River.
-
B.
Hässleholm
Hässleholm is a town in southern Sweden’s Skåne County known as a regional railway hub and service center.
-
C.
Karlshamn
Karlshamn is a coastal town in southern Sweden known for its harbor, archipelago, and role as a regional industrial and transport hub.
-
D.
Oskarshamn
Oskarshamn is a coastal town in southeastern Sweden known for its Baltic Sea harbor and proximity to the island of Gotland.
-
E.
Strängnäs
Strängnäs is a historic Swedish town known for its medieval cathedral and picturesque location on the shores of Lake Mälaren.
- 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_69e0b50b53048190ae34e8abbe3c5ada |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7223b4c4c8190b9fffa610588651e |
completed | April 21, 2026, 7:07 a.m. |
Created at: April 16, 2026, 2:56 p.m.