Triple
T21720142
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marmara District |
E536131
|
entity |
| Predicate | hasIslandSettlement |
P16159
|
FINISHED |
| Object | Avşa |
—
|
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: Avşa | Statement: [Marmara District, hasIslandSettlement, Avşa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Avşa Context triple: [Marmara District, hasIslandSettlement, Avşa]
-
A.
Avşa
chosen
Avşa is a small coastal settlement on Avşa Island in Turkey, known for its beaches and tourism.
-
B.
Ortaca
Ortaca is a town and district in southwestern Turkey known for its proximity to popular Aegean and Mediterranean coastal resorts and natural attractions.
-
C.
Alaşehir
Alaşehir is a town and district in Manisa Province in western Turkey, known for its agricultural production and historical roots dating back to ancient times.
-
D.
Havza
Havza is a district and town in northern Turkey known for its thermal springs and location within Samsun Province in the Black Sea region.
-
E.
Suşehri
Suşehri is a town and district in northeastern Turkey known for its location within Sivas Province and its surrounding mountainous landscape.
- 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_69e0c46c6dd88190a595375fa6ebd701 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69efd96de818819084c268d4775a8e3a |
completed | April 27, 2026, 9:47 p.m. |
Created at: April 16, 2026, 6:47 p.m.