Triple
T19804147
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Şehitkamil |
E475765
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Şahinbey |
—
|
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: Şahinbey | Statement: [Şehitkamil, borders, Şahinbey]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Şahinbey Context triple: [Şehitkamil, borders, Şahinbey]
-
A.
Şahinbey
chosen
Şahinbey is a central district and municipality of Gaziantep in southeastern Turkey, known as a major urban and commercial area of the city.
-
B.
Cihanbeyli
Cihanbeyli is a town and district in central Turkey known for its location on the Konya Plain and its agriculture-based local economy.
-
C.
Muratpaşa
Muratpaşa is a central district and municipality of the city of Antalya in southern Turkey, known for its coastal location and urban, touristic character.
-
D.
Orhangazi
Orhangazi is a town and district in northwestern Turkey known for its olive cultivation and location near Lake İznik in Bursa Province.
-
E.
Dursunbey
Dursunbey is a town and district in western Turkey known for its forestry, timber production, and rural character within Balıkesir Province.
- 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_69d8e51bc4208190a1c57d8c5d1b15e4 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e654266e18819085698aed8b0e2ba8 |
completed | April 20, 2026, 4:28 p.m. |
Created at: April 10, 2026, 1:49 p.m.