Triple
T19816875
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Skeppsholmen |
E476079
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Djurgården |
—
|
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: Djurgården | Statement: [Skeppsholmen, near, Djurgården]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Djurgården Context triple: [Skeppsholmen, near, Djurgården]
-
A.
Djurgården
chosen
Djurgården is a central Stockholm island known for its parks, museums, and major attractions like the Vasa Museum and Skansen.
-
B.
Djurgårdens IF
Djurgårdens IF is a prominent Swedish sports club from Stockholm, best known for its successful ice hockey and football teams and large, passionate fan base.
-
C.
Hammarby IF
Hammarby IF is a Swedish sports club from Stockholm best known for its passionate fan base and prominent football team competing in the country’s top divisions.
-
D.
IFK Göteborg
IFK Göteborg is a prominent Swedish football club based in Gothenburg, known for its domestic success and historic UEFA Cup victories.
-
E.
Kalmar FF
Kalmar FF is a professional Swedish football club based in the city of Kalmar that competes in the country’s top leagues and national competitions.
- 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_69d8e51bc4208190a1c57d8c5d1b15e4 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e654f9c5b08190987237f5144c3b37 |
completed | April 20, 2026, 4:31 p.m. |
Created at: April 10, 2026, 1:50 p.m.