Triple
T12807920
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Akşehir |
E306192
|
entity |
| Predicate | hasNameInTurkish |
P15502
|
FINISHED |
| Object | Akşehir |
E306192
|
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: Akşehir | Statement: [Akşehir, hasNameInTurkish, Akşehir]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Akşehir Context triple: [Akşehir, hasNameInTurkish, Akşehir]
-
A.
Akşehir
chosen
Akşehir is a town in central Turkey historically notable as a key strategic hub during the Turkish War of Independence.
-
B.
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.
-
C.
Seydişehir
Seydişehir is a town and district in central Turkey known for its aluminum industry and location within Konya Province.
-
D.
Suşehri
Suşehri is a town and district in northeastern Turkey known for its location within Sivas Province and its surrounding mountainous landscape.
-
E.
Keçiören
Keçiören is a densely populated metropolitan district and municipality of Ankara, known as one of the capital city’s major residential and commercial areas.
- 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_69d7bdf46c448190b1faa55aaacb6317 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96e808130819080f404b3a7462c2e |
completed | April 10, 2026, 9:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7b05474bc8190a42e2a9540055c47 |
completed | May 3, 2026, 8:30 p.m. |
Created at: April 9, 2026, 5:31 p.m.