Triple
T19738996
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Şahinbey |
E474061
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Şehitkamil |
—
|
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: Şehitkamil | Statement: [Şahinbey, borders, Şehitkamil]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Şehitkamil Context triple: [Şahinbey, borders, Şehitkamil]
-
A.
Şehitkamil
chosen
Şehitkamil is a central district and municipality of Gaziantep in southeastern Turkey, known for its urban development and cultural institutions.
-
B.
Battalgazi
Battalgazi is a historic district and town in eastern Turkey known for its ancient city of Melitene and significant Seljuk-era architectural heritage.
-
C.
Kilis
Kilis is a small Turkish city near the Syrian border known for its strategic location, cross-border trade, and distinctive regional cuisine.
-
D.
Güzelyurt
Güzelyurt is a town in the northwestern part of Cyprus, known for its citrus orchards and archaeological sites.
-
E.
Güzelyurt
Güzelyurt is a historic town in Turkey’s Cappadocia region, known for its rock-cut churches, underground cities, and scenic valleys.
- 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_69d8e517ebd48190979ee76723bcfadf |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6515f6efc8190a3da113847464399 |
completed | April 20, 2026, 4:16 p.m. |
Created at: April 10, 2026, 1:47 p.m.