Triple
T22333665
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Viskan |
E552087
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Borås |
—
|
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: Borås | Statement: [Viskan, flowsThrough, Borås]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Borås Context triple: [Viskan, flowsThrough, Borås]
-
A.
Borås
chosen
Borås is a Swedish city known for its textile industry heritage, design and fashion focus, and location in western Sweden.
-
B.
Västerås
Västerås is a historic city in central Sweden known for its medieval cathedral, lakeside location on Lake Mälaren, and role as an important industrial and commercial center.
-
C.
Örebro
Örebro is a historic city in central Sweden known for its medieval castle, university, and role as a regional economic and cultural hub.
-
D.
Skövde
Skövde is a town in south-central Sweden that serves as a major military hub and training center for the Swedish Army.
-
E.
Norrköping
Norrköping is a historic industrial city in eastern Sweden known for its preserved textile mills, waterways, and cultural institutions.
- 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_69e11e482f788190b78d1588fc26d606 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f1577cdcb08190a760e195c1051adb |
completed | April 29, 2026, 12:57 a.m. |
Created at: April 16, 2026, 8:43 p.m.