Triple
T13207696
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sarapul |
E314407
|
entity |
| Predicate | locatedEastOf |
P4240
|
FINISHED |
| Object |
Kazan
Kazan is the capital and largest city of Russia’s Republic of Tatarstan, known as a major cultural, economic, and educational center on the Volga River.
|
E35521
|
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: Kazan | Statement: [Sarapul, locatedEastOf, Kazan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kazan Context triple: [Sarapul, locatedEastOf, Kazan]
-
A.
Kazan
Kazan is a major city in western Russia and the capital of the Republic of Tatarstan, known for its rich Tatar-Russian cultural heritage and historic Kremlin.
-
B.
Kazanh
Kazanh is a locality within Turkey’s Ankara Province, situated in the Central Anatolia region.
-
C.
Ufa
Ufa is the capital and largest city of the Republic of Bashkortostan in Russia, known as a major industrial, cultural, and economic center in the Ural region.
-
D.
Naberezhnye Chelny
Naberezhnye Chelny is a major industrial city in Russia’s Republic of Tatarstan, best known as the home of the KamAZ truck manufacturing plant.
-
E.
Kazanin
Kazanin is a Russian-language surname most notably borne by comedian and television personality Stepan Kazanin.
- 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: Kazan Triple: [Sarapul, locatedEastOf, Kazan]
Generated description
Kazan is the capital and largest city of Russia’s Republic of Tatarstan, known as a major cultural, economic, and educational center on the Volga River.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kazan Target entity description: Kazan is the capital and largest city of Russia’s Republic of Tatarstan, known as a major cultural, economic, and educational center on the Volga River.
-
A.
Kazan
chosen
Kazan is a major city in western Russia and the capital of the Republic of Tatarstan, known for its rich Tatar-Russian cultural heritage and historic Kremlin.
-
B.
Kazanh
Kazanh is a locality within Turkey’s Ankara Province, situated in the Central Anatolia region.
-
C.
Ufa
Ufa is the capital and largest city of the Republic of Bashkortostan in Russia, known as a major industrial, cultural, and economic center in the Ural region.
-
D.
Naberezhnye Chelny
Naberezhnye Chelny is a major industrial city in Russia’s Republic of Tatarstan, best known as the home of the KamAZ truck manufacturing plant.
-
E.
Kazanin
Kazanin is a Russian-language surname most notably borne by comedian and television personality Stepan Kazanin.
- F. None of above.
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_69d806aee7308190b70a237ba2a6e3e1 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98c9cb7ac819095cff8699993c419 |
completed | April 10, 2026, 11:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcb63f006c8190997c0f3cdc6efd6c |
completed | May 7, 2026, 3:56 p.m. |
| NEDg | Description generation | batch_69fcc4734d388190b4a06799e362cfa9 |
completed | May 7, 2026, 4:57 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fcc51f30588190bfe6efd1dc84d921 |
completed | May 7, 2026, 5 p.m. |
Created at: April 9, 2026, 9:17 p.m.