Triple
T19945068
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Municipality of Florina |
E479401
|
entity |
| Predicate | containsSettlement |
P847
|
FINISHED |
| Object | Kato Kleines |
—
|
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: Kato Kleines | Statement: [Municipality of Florina, containsSettlement, Kato Kleines]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kato Kleines Context triple: [Municipality of Florina, containsSettlement, Kato Kleines]
-
A.
Kato Kleines
chosen
Kato Kleines is a village located in the Florina regional unit of Western Macedonia in northern Greece.
-
B.
Kato
Kato is a Japanese surname shared by numerous notable individuals across fields such as entertainment, sports, and politics.
-
C.
Kato
Kato is the nickname of Kato Svanidze, who was the first wife of Soviet leader Joseph Stalin.
-
D.
Kato
Kato is an indigenous Athabaskan language traditionally spoken in northern California by the Kato people.
-
E.
Kato
Kato is the highly skilled martial artist, mechanic, and sidekick who partners with the Green Hornet to fight crime.
- 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_69d8e522a17c819095165d4d24939fd8 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65a6654f481908089e1d66e17e024 |
completed | April 20, 2026, 4:55 p.m. |
Created at: April 10, 2026, 1:54 p.m.