Triple
T20687794
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rubin Kazan |
E508466
|
entity |
| Predicate | basedIn |
P40
|
FINISHED |
| Object | Kazan |
—
|
NE NERFINISHED |
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: Kazan | Statement: [Rubin Kazan, basedIn, Kazan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kazan Context triple: [Rubin Kazan, basedIn, Kazan]
-
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.
Kazan
Kazan is a surname most notably associated with a family of American artists and filmmakers, including screenwriter Nicholas Kazan and director Elia Kazan.
-
C.
Kazanh
Kazanh is a locality within Turkey’s Ankara Province, situated in the Central Anatolia region.
-
D.
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.
-
E.
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.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4c1ed408190b72dd26b1e33f8a1 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6beaddfe88190897b8963fa8b2245 |
completed | April 21, 2026, 12:02 a.m. |
Created at: April 16, 2026, 11:45 a.m.