Triple
T2581769
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Samara River |
E57106
|
entity |
| Predicate | RussianName |
P744
|
FINISHED |
| Object | Самара |
E67593
|
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: Самара | Statement: [Samara River, RussianName, Самара]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Самара Context triple: [Samara River, RussianName, Самара]
-
A.
Saratov
Saratov is a major city in southwestern Russia known as an important cultural, educational, and industrial center on the banks of the Volga River.
-
B.
Nizhny Novgorod
Nizhny Novgorod is a major Russian city located at the confluence of the Volga and Oka rivers, known for its historic Kremlin, industrial significance, and role as a key cultural and economic center in the Volga region.
-
C.
Samara
chosen
Samara is a major Russian city on the Volga River known as an important industrial, cultural, and transportation hub.
-
D.
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.
-
E.
Magnitogorsk
Magnitogorsk is a major industrial city in Russia’s Chelyabinsk Oblast, historically centered around one of the world’s largest iron and steel works.
- 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_69ab4a4dca6481908c301f8e317396e7 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd3c843bc8190837cea3441bf3ca1 |
completed | March 7, 2026, 7:29 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b432e768308190a5476e660111e573 |
completed | March 13, 2026, 3:53 p.m. |
Created at: March 6, 2026, 9:49 p.m.