Triple
T7042758
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bebek |
E163551
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Arnavutköy |
E238391
|
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: Arnavutköy | Statement: [Bebek, near, Arnavutköy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Arnavutköy Context triple: [Bebek, near, Arnavutköy]
-
A.
Arnavutköy
chosen
Arnavutköy is a district on the European side of Istanbul, Turkey, known for its rapidly developing urban areas and hosting the city’s main international airport.
-
B.
Ayvacık
Ayvacık is a small town and district in Turkey’s Çanakkale Province, known for its traditional stone houses and proximity to the Aegean coast and ancient sites like Assos.
-
C.
Florya
Florya is a coastal neighborhood in Istanbul, Turkey, known for its residential areas, seaside promenade, and recreational facilities.
-
D.
Gazipaşa
Gazipaşa is a coastal town and district in Antalya Province, southern Turkey, known for its Mediterranean beaches, agricultural production, and proximity to ancient ruins.
-
E.
Torbalı
Torbalı is a district and rapidly growing suburban area of İzmir, Turkey, known for its industrial zones and connection to the city via the İZBAN commuter rail system.
- 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_69c6885e7c1c8190be32a8f79ab4e0cf |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e235a2e08190bb049ee6e719f0f9 |
completed | March 27, 2026, 8:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7b8c5a9fc81909a94e8e6c287b591 |
completed | March 28, 2026, 11:17 a.m. |
Created at: March 27, 2026, 2:36 p.m.