Triple
T1030121
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dodecanese |
E22229
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Kos town |
E74968
|
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: Kos town | Statement: [Dodecanese, hasCity, Kos town]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kos town Context triple: [Dodecanese, hasCity, Kos town]
-
A.
Kos
chosen
Kos is a Greek island in the southeastern Aegean Sea known for its sandy beaches, ancient ruins, and vibrant tourism.
-
B.
Alushta
Alushta is a resort town on the southern coast of Crimea, known for its beaches, mild climate, and role as a popular Black Sea tourist destination.
-
C.
Kutaisi
Kutaisi is one of Georgia’s major cities, historically significant and formerly a capital, located in the western part of the country.
-
D.
Salen
Salen is a small coastal village on the Isle of Mull in Scotland, known as a local hub with basic services for residents and visitors exploring the island.
-
E.
Konak
Konak is the central historic district of İzmir, Turkey, known for its waterfront, clock tower, and role as the city’s administrative and cultural hub.
- 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_69a493d848848190aed4011b34b2e8d3 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b7f962608190b3ebd9140472b979 |
completed | March 1, 2026, 10:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac429675bc8190b2467ac86c41c3b5 |
completed | March 7, 2026, 3:21 p.m. |
Created at: March 1, 2026, 7:41 p.m.