Triple
T19774577
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tralles |
E474969
|
entity |
| Predicate | nearModernCity |
P8489
|
FINISHED |
| Object | Aydın |
—
|
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: Aydın | Statement: [Tralles, nearModernCity, Aydın]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aydın Context triple: [Tralles, nearModernCity, Aydın]
-
A.
Afyon
Afyon is the former name of Afyonkarahisar, a city in western Turkey known for its strategic location, thermal springs, and opium poppy production.
-
B.
Aydın Province
chosen
Aydın Province is a region in western Turkey known for its fertile plains, Aegean coastline, and rich ancient heritage, including the historic area once part of Caria.
-
C.
Aksaray
Aksaray is a historic city in central Turkey known for its location on the ancient Silk Road and its proximity to the Cappadocia region.
-
D.
Güzelyurt
Güzelyurt is a historic town in Turkey’s Cappadocia region, known for its rock-cut churches, underground cities, and scenic valleys.
-
E.
Güzelyurt
Güzelyurt is a town in the northwestern part of Cyprus, known for its citrus orchards and archaeological sites.
- 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_69d8e51a43a08190956bc6df13c91a77 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6535effcc819080a71de148759674 |
completed | April 20, 2026, 4:25 p.m. |
Created at: April 10, 2026, 1:48 p.m.