Triple
T13251207
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kızılay |
E315533
|
entity |
| Predicate | hasLandmark |
P105
|
FINISHED |
| Object | Kızılay Square |
E1029909
|
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: Kızılay Square | Statement: [Kızılay, hasLandmark, Kızılay Square]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kızılay Square Context triple: [Kızılay, hasLandmark, Kızılay Square]
-
A.
Kızılay Square
chosen
Kızılay Square is a major central square and transportation hub in Ankara, Turkey, known as one of the city's main commercial and social gathering points.
-
B.
Krasnaya Square
Krasnaya Square is the former name of Minin and Pozharsky Square, a central public square in Nizhny Novgorod, Russia.
-
C.
Vosstaniya Square
Vosstaniya Square is a major public square and transport hub in central Saint Petersburg, Russia, known for its proximity to Moskovsky railway station and busy Nevsky Prospekt.
-
D.
Sovetskaya Square
Sovetskaya Square was the former name of Minin and Pozharsky Square, a central public square in Nizhny Novgorod, Russia, known for its historical and cultural significance.
-
E.
Komsomolskaya Square
Komsomolskaya Square is a major transport hub in Moscow known for its concentration of railway stations and heavy commuter traffic.
- 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_69f78acfd0e88190954533a7f282d83a |
completed | May 3, 2026, 5:50 p.m. |
Created at: April 9, 2026, 9:24 p.m.