Triple
T13251213
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kızılay |
E315533
|
entity |
| Predicate | hasLandmark |
P105
|
FINISHED |
| Object | Sakarya Street |
E1031015
|
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: Sakarya Street | Statement: [Kızılay, hasLandmark, Sakarya Street]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sakarya Street Context triple: [Kızılay, hasLandmark, Sakarya Street]
-
A.
Kadife Street
Kadife Street is a popular nightlife and entertainment street in Istanbul’s Kadıköy district, known for its bars, live music venues, and vibrant youth culture.
-
B.
Mithatpaşa Street
Mithatpaşa Street is a major coastal thoroughfare in İzmir, Turkey, known for its scenic views along the Gulf and its role as a key artery through several central neighborhoods.
-
C.
Doganbey Street
Doganbey Street is a local street in the Anıttepe neighborhood, an urban area of Ankara, Turkey.
-
D.
Selanik Street
chosen
Selanik Street is a well-known central street in Ankara’s Kızılay district, noted for its shops, cafes, and busy urban atmosphere.
-
E.
Sinan Road
Sinan Road is a historic, tree-lined street in Shanghai known for its well-preserved early 20th-century architecture and cultural landmarks.
- 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_69d806b1072881909e46bd212259c5f0 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98f73423c8190932a9edac56df383 |
completed | April 11, 2026, 12:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f716c6797c819090bfcc9a5b62ac1c |
completed | May 3, 2026, 9:35 a.m. |
Created at: April 9, 2026, 9:24 p.m.