Triple
T7429496
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sarıyer |
E171451
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Maslak |
E646551
|
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: Maslak | Statement: [Sarıyer, contains, Maslak]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maslak Context triple: [Sarıyer, contains, Maslak]
-
A.
Maslak
chosen
Maslak is a major business and financial district in Istanbul, Turkey, known for its skyscrapers, corporate offices, and modern commercial complexes.
-
B.
Yenimahalle
Yenimahalle is a major district of Ankara, Turkey, known for hosting key government institutions and residential areas within the capital.
-
C.
Bakırköy
Bakırköy is a coastal district on the European side of Istanbul, Turkey, known for its residential neighborhoods, shopping centers, and seaside recreation areas.
-
D.
Budapest 13th district
Budapest 13th district is a central, densely populated district of Hungary’s capital, known for its mix of residential neighborhoods, business areas, and sections along the Danube River.
-
E.
Ortaköy
Ortaköy is a lively Bosphorus-side neighborhood in Istanbul known for its waterfront mosque, cafes, and views of the Bosporus Bridge.
- 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_69c68a63491881909281f73d4d5643bf |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f3082f188190af5673d18ac7e87e |
completed | March 27, 2026, 9:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c81f135a348190ae9edc02a19b2278 |
completed | March 28, 2026, 6:33 p.m. |
Created at: March 27, 2026, 3:12 p.m.