Triple
T16017252
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North Kynouria |
E388498
|
entity |
| Predicate | containsSettlement |
P847
|
FINISHED |
| Object | Kato Vervena |
E1190316
|
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: Kato Vervena | Statement: [North Kynouria, containsSettlement, Kato Vervena]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kato Vervena Context triple: [North Kynouria, containsSettlement, Kato Vervena]
-
A.
Kato Vervena
chosen
Kato Vervena is a village in the municipality of North Kynouria in the Arcadia regional unit of the Peloponnese, Greece.
-
B.
Ano Vervena
Ano Vervena is a small mountain village in the municipality of North Kynouria in the Arcadia region of the Peloponnese, Greece.
-
C.
Verdolagas
Verdolagas is the popular nickname of Honduran football club Marathón, one of the country’s most traditional and successful teams.
-
D.
Patachou
Patachou was a celebrated French singer, actress, and Parisian cabaret owner known for her warm voice and interpretations of classic chanson.
-
E.
Verver
Verver is the surname of Maggie Verver, a central character in Henry James’s novel "The Golden Bowl."
- 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_69d86dabcb7c8190b6a39d6831d2fa1b |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e18295c6a4819093263db8669d4b08 |
completed | April 17, 2026, 12:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffe470cd5881909cc0c48b3a540d61 |
completed | May 10, 2026, 1:50 a.m. |
Created at: April 10, 2026, 4:55 a.m.