Triple
T3738778
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NEVS |
E79648
|
entity |
| Predicate | headquartersLocation |
P62
|
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: [NEVS, headquartersLocation, Trollhättan, Sweden]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Trollhättan, Sweden Context triple: [NEVS, headquartersLocation, 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.
Södertälje, Sweden
Södertälje, Sweden is an industrial city southwest of Stockholm known for its major manufacturing plants, particularly in the automotive and heavy vehicle sectors.
-
C.
Karlskoga, Sweden
Karlskoga, Sweden is an industrial town in central Sweden best known for its historic arms manufacturer Bofors and its association with Alfred Nobel.
-
D.
Hudiksvall
Hudiksvall is a coastal town in east-central Sweden known for its historic wooden buildings and harbor on the Gulf of Bothnia.
-
E.
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.
- 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_69ad8b115610819095b02007da5ca3cb |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcb404b908190b6b4ee583dee3cc9 |
completed | March 8, 2026, 7:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4db20bdfc81909cd27278ff5d9716 |
completed | March 14, 2026, 3:50 a.m. |
Created at: March 8, 2026, 3:34 p.m.