Triple
T7770557
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gävle |
E179056
|
entity |
| Predicate | hasRailConnectionTo |
P848
|
FINISHED |
| Object | Uppsala |
E36359
|
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: Uppsala | Statement: [Gävle, hasRailConnectionTo, Uppsala]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Uppsala Context triple: [Gävle, hasRailConnectionTo, Uppsala]
-
A.
Uppsala
chosen
Uppsala is a historic Swedish city north of Stockholm, known for its prestigious university, medieval cathedral, and role as a cultural and ecclesiastical center.
-
B.
Karlstad
Karlstad is a city in central Sweden known as the capital of Värmland County, situated on the northern shore of Lake Vänern.
-
C.
Sundsvall
Sundsvall is a coastal city in central Sweden known as an important industrial and commercial center on the Gulf of Bothnia.
-
D.
Lund
Lund is a common Scandinavian surname of Swedish origin.
-
E.
Lund
Lund is a historic city in southern Sweden known for its medieval cathedral, prestigious university, and role as a significant cultural and academic center in Scandinavia.
- 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_69c69f30602c819082ab52cd4af5c592 |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c70438ca2481909114b0c434717109 |
completed | March 27, 2026, 10:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc932b974081908d2cb160a670eb01 |
completed | April 1, 2026, 3:38 a.m. |
Created at: March 27, 2026, 4:11 p.m.