Triple
T20135893
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Red line (Stockholm metro) |
E491023
|
entity |
| Predicate | passesThroughStation |
P3947
|
FINISHED |
| Object | Karlaplan |
—
|
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: Karlaplan | Statement: [Red line (Stockholm metro), passesThroughStation, Karlaplan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Karlaplan Context triple: [Red line (Stockholm metro), passesThroughStation, Karlaplan]
-
A.
Karlaplan
chosen
Karlaplan is a prominent circular plaza and park with a central fountain in the Östermalm district of Stockholm, Sweden.
-
B.
Lanke
Lanke is a village and district within the municipality of Wandlitz in the state of Brandenburg, Germany.
-
C.
Lanke
Lanke is a poetic nickname for the Chinese city of Quzhou, often associated with its cultural heritage and scenic landscapes.
-
D.
Kluuvi
Kluuvi is a central district of Helsinki, Finland, known as the city’s main commercial and business hub.
-
E.
Karesi
Karesi is a central district and municipality of Balıkesir in western Turkey, known for its role as an administrative and commercial hub of the province.
- 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_69da62651a0c8190a3e05e95e056a66b |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e66766e46c81908721fd47066dc9f8 |
completed | April 20, 2026, 5:50 p.m. |
Created at: April 11, 2026, 11:32 p.m.