Triple
T13038372
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Swedish Air Force F 21 Wing |
E326624
|
entity |
| Predicate | garrison |
P75
|
FINISHED |
| Object | Luleå |
E160272
|
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: Luleå | Statement: [Swedish Air Force F 21 Wing, garrison, Luleå]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Luleå Context triple: [Swedish Air Force F 21 Wing, garrison, Luleå]
-
A.
Luleå
chosen
Luleå is a coastal city in northern Sweden known for its major port, technology and university hub, and proximity to the Arctic Circle.
-
B.
Umeå
Umeå is a university city in northern Sweden known for its cultural scene, research institutions, and role as a regional economic hub.
-
C.
Skellefteå
Skellefteå is a city in northern Sweden known for its growing high-tech and green industry sector, particularly in battery manufacturing, as well as its ice hockey tradition.
-
D.
Piteå
Piteå is a coastal town in northern Sweden known for its historic wooden architecture, archipelago, and role as a regional cultural and industrial center in Norrbotten County.
-
E.
Pajala
Pajala is a small town in northern Sweden’s Lapland region, known for its remote Arctic setting and as the backdrop of several works by author Mikael Niemi.
- 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_69d8076cc45c81908123123f43e69266 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d9804b743c8190810dc5c14bc6d912 |
completed | April 10, 2026, 10:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6f5c5155c8190b2c4e5bbcdaadc47 |
completed | May 3, 2026, 7:14 a.m. |
Created at: April 9, 2026, 8:55 p.m.