Triple
T811991
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saab 93 |
E17564
|
entity |
| Predicate | productionLocation |
P3992
|
FINISHED |
| Object | Trollhättan, Sweden |
E18308
|
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: Trollhättan, Sweden | Statement: [Saab 93, productionLocation, Trollhättan, Sweden]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Trollhättan, Sweden Context triple: [Saab 93, productionLocation, Trollhättan, Sweden]
-
A.
Trollhättan
chosen
Trollhättan is a city in western Sweden known for its historic role in the automotive industry and as the longtime home of Saab Automobile’s main production facilities.
-
B.
Växjö, Sweden
Växjö is a mid-sized city in southern Sweden known for its environmental sustainability initiatives, universities, and role as a regional cultural and economic center in Småland.
-
C.
Strömstad
Strömstad is a coastal town and municipality in western Sweden, near the Norwegian border, known for its archipelago, tourism, and ferry connections.
-
D.
Östersund
Östersund is a city in central Sweden known for its strong winter sports tradition and repeated bids to host the Winter Olympics.
-
E.
Linköping
Linköping is a major city in southern Sweden known for its university, high-tech industry, and historic cathedral.
- 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_69a4937ae8a08190b5084a03d532b30e |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4ab4c7418819085cb64c6bf5fa70c |
completed | March 1, 2026, 9:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7928d2ee8819091dbef1dd5272f6f |
completed | March 4, 2026, 2:01 a.m. |
Created at: March 1, 2026, 7:38 p.m.