Triple

T10153093
Position Surface form Disambiguated ID Type / Status
Subject Kazan River E232699 entity
Predicate flowsThrough P225 FINISHED
Object 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.
 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: [Kazan River, flowsThrough, Kazan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kazan
Context triple: [Kazan River, flowsThrough, 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: [Kazan River, flowsThrough, Kazan]
Generated description
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.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kazan
Target entity description: 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.
  • 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_69ca84885e48819088a31b127cf44904 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cdec376cd48190990862c56c3a4dce completed April 2, 2026, 4:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69de54a30b748190bb791078e9dde442 completed April 14, 2026, 2:52 p.m.
NEDg Description generation batch_69de5952f6c48190abd3b87372d54f58 completed April 14, 2026, 3:12 p.m.
NED2 Entity disambiguation (via description) batch_69de5ed49c9c8190a4085407f88d7a05 completed April 14, 2026, 3:35 p.m.
Created at: March 30, 2026, 9:08 p.m.