Triple
T1947240
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | My Universities |
E42083
|
entity |
| Predicate | setting |
P1957
|
FINISHED |
| Object | Kazan |
E35521
|
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: Kazan | Statement: [My Universities, setting, Kazan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kazan Context triple: [My Universities, setting, 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.
Kaspiysk
Kaspiysk is a coastal city on the Caspian Sea in the Republic of Dagestan, Russia, known for its industrial base and strategic naval facilities.
-
C.
Alma-Atinskaya
Alma-Atinskaya is a southern terminus station of the Moscow Metro, serving as one endpoint of the Zamoskvoretskaya Line.
-
D.
Kirov
Kirov is the revolutionary pseudonym of Sergei Kirov, a prominent early Soviet political leader and close associate of Joseph Stalin.
-
E.
Samara
Samara is a design-focused housing and urban innovation company co-founded by Airbnb’s Joe Gebbia to explore new forms of living and community.
- 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_69a8870e08fc8190a319cbf2600db15f |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb32ebae881908f7541301f0198ae |
completed | March 7, 2026, 5:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afce7154648190aa55d54ca5e50559 |
completed | March 10, 2026, 7:55 a.m. |
Created at: March 4, 2026, 7:36 p.m.