Triple
T17089762
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Turkish high-speed rail network |
E414692
|
entity |
| Predicate | connectsCity |
P4245
|
FINISHED |
| Object | Arifiye |
E823361
|
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: Arifiye | Statement: [Turkish high-speed rail network, connectsCity, Arifiye]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Arifiye Context triple: [Turkish high-speed rail network, connectsCity, Arifiye]
-
A.
Arifiye
chosen
Arifiye is a town and district in northwestern Turkey, located within Sakarya Province and known for its growing industrial and residential areas.
-
B.
Güzelyurt
Güzelyurt is a town in the northwestern part of Cyprus, known for its citrus orchards and archaeological sites.
-
C.
Güzelyurt
Güzelyurt is a historic town in Turkey’s Cappadocia region, known for its rock-cut churches, underground cities, and scenic valleys.
-
D.
Ayvalık
Ayvalık is a coastal town in northwestern Turkey known for its historic Greek architecture, olive oil production, and scenic Aegean Sea views.
-
E.
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.
- 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_69d886cfc8e88190b05ba466edd35591 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dbf948d88190a4dd4a0774a8505b |
completed | April 18, 2026, 7:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a012ee9fd108190b12e8624bb66caf2 |
completed | May 11, 2026, 1:20 a.m. |
Created at: April 10, 2026, 5:35 a.m.