Triple
T19680719
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Skara |
E472576
|
entity |
| Predicate | nearbyCity |
P350
|
FINISHED |
| Object | Skövde |
—
|
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: Skövde | Statement: [Skara, nearbyCity, Skövde]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Skövde Context triple: [Skara, nearbyCity, Skövde]
-
A.
Skövde
chosen
Skövde is a town in south-central Sweden that serves as a major military hub and training center for the Swedish Army.
-
B.
Sundsvall
Sundsvall is a coastal city in central Sweden known as an important industrial and commercial center on the Gulf of Bothnia.
-
C.
Växjö
Växjö is a city in southern Sweden known for its lakeside setting, environmental sustainability initiatives, and role as a regional cultural and educational center.
-
D.
Norrköping
Norrköping is a historic industrial city in eastern Sweden known for its preserved textile mills, waterways, and cultural institutions.
-
E.
Örebro
Örebro is a historic city in central Sweden known for its medieval castle, university, and role as a regional economic and cultural hub.
- 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_69d8e514f2e08190ba70a4449519d218 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e641bf97348190bc31b00ed4ec6cad |
completed | April 20, 2026, 3:09 p.m. |
Created at: April 10, 2026, 1:45 p.m.