Triple
T21128974
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | City of Kemi |
E520632
|
entity |
| Predicate | hasNeighbouringMunicipality |
P224
|
FINISHED |
| Object | Simo |
—
|
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: Simo | Statement: [City of Kemi, hasNeighbouringMunicipality, Simo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Simo Context triple: [City of Kemi, hasNeighbouringMunicipality, Simo]
-
A.
Simo
chosen
Simo is a Finnish given name most famously borne by Simo Häyhä, a legendary World War II sniper.
-
B.
Simoni
Simoni is an Italian surname most notably associated with Gigi Simoni, a respected former football player and manager.
-
C.
Simm
Simm is an English surname most notably associated with actor John Simm, known for his roles in British television and film.
-
D.
Sven
Sven is the lovable reindeer companion in Disney's animated film "Frozen," known for his close bond with Kristoff and his expressive, dog-like personality.
-
E.
Sven
Sven is a charismatic puffin in the animated film "Happy Feet Two," admired by other characters for his apparent ability to fly and his inspirational persona.
- 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_69e0b50b53048190ae34e8abbe3c5ada |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7223b4c4c8190b9fffa610588651e |
completed | April 21, 2026, 7:07 a.m. |
Created at: April 16, 2026, 2:56 p.m.