Triple
T22660737
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ermioni |
E559653
|
entity |
| Predicate | nearby |
P350
|
FINISHED |
| Object | Dokos |
—
|
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: Dokos | Statement: [Ermioni, nearby, Dokos]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dokos Context triple: [Ermioni, nearby, Dokos]
-
A.
Dokos
chosen
Dokos is a small, sparsely inhabited Greek island in the Saronic Gulf, known for its rugged landscape and archaeological significance.
-
B.
Dokka
Dokka is a small Norwegian town that serves as a local commercial and service center in the inland region of Oppland.
-
C.
Doscos
Doscos are alumni of The Doon School, an elite boarding school in Dehradun, India.
-
D.
Doksy
Doksy is a small Czech town in the Liberec Region, known for its proximity to Lake Mácha and its popularity as a recreational and holiday destination.
-
E.
Dokki
Dokki is a prominent district in Giza, Egypt, known for its government institutions, educational centers, and residential neighborhoods just across the Nile from central Cairo.
- 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_69e2454a158c819093b8e35f5045efb6 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1765edf88819086c28525e3c73758 |
completed | April 29, 2026, 3:09 a.m. |
Created at: April 17, 2026, 3:07 p.m.