Triple
T15050555
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nor Bayazet |
E379347
|
entity |
| Predicate | hasNameInRussian |
P20560
|
FINISHED |
| Object |
Нор-Баязет
Нор-Баязет — это историческое название армянского города Гавар, расположенного в Гегаркуникской области у озера Севан.
|
E1173706
|
NE FINISHED |
How this triple was built (4 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: Нор-Баязет | Statement: [Nor Bayazet, hasNameInRussian, Нор-Баязет]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Нор-Баязет Context triple: [Nor Bayazet, hasNameInRussian, Нор-Баязет]
-
A.
Orhangazi
Orhangazi is a town and district in northwestern Turkey known for its olive cultivation and location near Lake İznik in Bursa Province.
-
B.
Mustafakemalpaşa
Mustafakemalpaşa is a town and district in northwestern Turkey known for its agricultural production and historical connection to Mustafa Kemal Atatürk.
-
C.
Beştepe
Beştepe is a neighborhood in Ankara, Turkey, best known as the site of the Turkish Presidential Complex.
-
D.
Bağçasaray
Bağçasaray is the Crimean Tatar name for Bakhchisaray, a historic town in Crimea that once served as the capital of the Crimean Khanate.
-
E.
Bayrampaşa
Bayrampaşa is a densely populated working- and middle-class district on Istanbul’s European side, known for its major transport links, industrial areas, and large bus terminal.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Нор-Баязет Triple: [Nor Bayazet, hasNameInRussian, Нор-Баязет]
Generated description
Нор-Баязет — это историческое название армянского города Гавар, расположенного в Гегаркуникской области у озера Севан.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Нор-Баязет Target entity description: Нор-Баязет — это историческое название армянского города Гавар, расположенного в Гегаркуникской области у озера Севан.
-
A.
Orhangazi
Orhangazi is a town and district in northwestern Turkey known for its olive cultivation and location near Lake İznik in Bursa Province.
-
B.
Mustafakemalpaşa
Mustafakemalpaşa is a town and district in northwestern Turkey known for its agricultural production and historical connection to Mustafa Kemal Atatürk.
-
C.
Beştepe
Beştepe is a neighborhood in Ankara, Turkey, best known as the site of the Turkish Presidential Complex.
-
D.
Bağçasaray
Bağçasaray is the Crimean Tatar name for Bakhchisaray, a historic town in Crimea that once served as the capital of the Crimean Khanate.
-
E.
Bayrampaşa
Bayrampaşa is a densely populated working- and middle-class district on Istanbul’s European side, known for its major transport links, industrial areas, and large bus terminal.
- F. None of above. chosen
Provenance (5 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_69d85cd64d108190853797a95c11cc45 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69deda8f71988190b4fe7f7de4ccb798 |
completed | April 15, 2026, 12:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff82dfbc28819090cf56f16b5e7c39 |
completed | May 9, 2026, 6:54 p.m. |
| NEDg | Description generation | batch_69ff83d929a48190aea75597b864d210 |
completed | May 9, 2026, 6:58 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff8469354c819080b8cfddb7c66be5 |
completed | May 9, 2026, 7 p.m. |
Created at: April 10, 2026, 3:01 a.m.