Triple
T5955657
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marmara Island |
E132506
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Marmara District |
E536131
|
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: Marmara District | Statement: [Marmara Island, partOf, Marmara District]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marmara District Context triple: [Marmara Island, partOf, Marmara District]
-
A.
Marmara District
chosen
Marmara District is an administrative district in Balıkesir Province, Turkey, encompassing several islands in the Sea of Marmara, including Avşa Island.
-
B.
Üsküdar
Üsküdar is a historic and densely populated district of Istanbul known for its waterfront along the Bosphorus, Ottoman-era mosques, and traditional neighborhoods.
-
C.
Fatih district
Fatih district is a historic central district on Istanbul’s European side, encompassing many of the city’s most significant Byzantine and Ottoman landmarks.
-
D.
Çankaya
Çankaya is a central district of Ankara, Turkey, known for housing key government institutions, foreign embassies, and major national landmarks.
-
E.
Ümraniye
Ümraniye is a densely populated residential and commercial district located on the Asian side of Istanbul, Turkey.
- 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_69c0086b05cc8190a8f36a96927a525c |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c039c1de80819085c97a0aa2d37f32 |
completed | March 22, 2026, 6:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c64b970c648190aacb0af2f1bcb7ff |
completed | March 27, 2026, 9:19 a.m. |
Created at: March 22, 2026, 4:02 p.m.