Triple
T16017231
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North Kynouria |
E388498
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Ano Vervena
Ano Vervena is a small mountain village in the municipality of North Kynouria in the Arcadia region of the Peloponnese, Greece.
|
E1191704
|
NE FINISHED |
How this triple was built (4 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: Ano Vervena | Statement: [North Kynouria, hasPart, Ano Vervena]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ano Vervena Context triple: [North Kynouria, hasPart, Ano Vervena]
-
A.
Kato Vervena
Kato Vervena is a village in the municipality of North Kynouria in the Arcadia regional unit of the Peloponnese, Greece.
-
B.
Verdolagas
Verdolagas is the popular nickname of Honduran football club Marathón, one of the country’s most traditional and successful teams.
-
C.
Verver
Verver is the surname of Maggie Verver, a central character in Henry James’s novel "The Golden Bowl."
-
D.
Schildkraut
Schildkraut is a surname most notably associated with Austrian-American actor Joseph Schildkraut, an Academy Award winner known for his work in early 20th-century film and theater.
-
E.
Verbena
Verbena is a small unincorporated community and historic town in Chilton County, Alabama, known for its 19th-century resort history and rural character.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Ano Vervena Triple: [North Kynouria, hasPart, Ano Vervena]
Generated description
Ano Vervena is a small mountain village in the municipality of North Kynouria in the Arcadia region of the Peloponnese, Greece.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ano Vervena Target entity description: Ano Vervena is a small mountain village in the municipality of North Kynouria in the Arcadia region of the Peloponnese, Greece.
-
A.
Kato Vervena
Kato Vervena is a village in the municipality of North Kynouria in the Arcadia regional unit of the Peloponnese, Greece.
-
B.
Verdolagas
Verdolagas is the popular nickname of Honduran football club Marathón, one of the country’s most traditional and successful teams.
-
C.
Verver
Verver is the surname of Maggie Verver, a central character in Henry James’s novel "The Golden Bowl."
-
D.
Schildkraut
Schildkraut is a surname most notably associated with Austrian-American actor Joseph Schildkraut, an Academy Award winner known for his work in early 20th-century film and theater.
-
E.
Verbena
Verbena is a small unincorporated community and historic town in Chilton County, Alabama, known for its 19th-century resort history and rural character.
- F. None of above. chosen
Provenance (5 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_69ffdbcdf2548190999a6d093c7fb64a |
completed | May 10, 2026, 1:13 a.m. |
| NEDg | Description generation | batch_69ffddf957d88190bc344e493e8d567e |
completed | May 10, 2026, 1:23 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffde49c5b481908294793326140c13 |
completed | May 10, 2026, 1:24 a.m. |
Created at: April 10, 2026, 4:55 a.m.