Triple
T20747650
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Darıca |
E510627
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Gebze |
—
|
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: Gebze | Statement: [Darıca, locatedNear, Gebze]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gebze Context triple: [Darıca, locatedNear, Gebze]
-
A.
Gebze
chosen
Gebze is an industrial city in Turkey’s Kocaeli Province, located east of Istanbul and known as a major manufacturing and logistics hub in the Marmara region.
-
B.
Halkalı
Halkalı is a western district of Istanbul, Turkey, known as a major residential area and transport hub featuring significant rail and road connections.
-
C.
Seydişehir
Seydişehir is a town and district in central Turkey known for its aluminum industry and location within Konya Province.
-
D.
Karacabey
Karacabey is a town and district in northwestern Turkey known for its agriculture and proximity to both the Marmara Sea and the city of Bursa.
-
E.
Ereğli
Ereğli is a district and town in central Turkey known for its agricultural production and location within Konya Province on the Central Anatolian plateau.
- 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_69e0b4c845e88190b4c5f3ae79291182 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c226fbf881909794eff3ee9e206b |
completed | April 21, 2026, 12:17 a.m. |
Created at: April 16, 2026, 12:33 p.m.