Triple
T3224649
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Samara |
E67593
|
entity |
| Predicate | governingBody |
P46
|
FINISHED |
| Object | Samara City Duma |
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: Samara City Duma | Statement: [Samara, governingBody, Samara City Duma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Samara City Duma Context triple: [Samara, governingBody, Samara City Duma]
-
A.
Samara
chosen
Samara is a major Russian city on the Volga River known as an important industrial, cultural, and transportation hub.
-
B.
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.
-
C.
Kirov
Kirov is the revolutionary pseudonym of Sergei Kirov, a prominent early Soviet political leader and close associate of Joseph Stalin.
-
D.
Kamyshin
Kamyshin is a significant industrial and river port city on the Volga River in southwestern Russia.
-
E.
Krasnopresnenskaya
Krasnopresnenskaya is a Moscow Metro station on the city’s circular Koltsevaya Line, known for its deep-level construction and Soviet-era architectural design.
- 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_69ad858c61888190a31196310d9b30b5 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adae1c51a48190b4a395650528b5d8 |
completed | March 8, 2026, 5:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2625eaa708190b23ca6e575d664a2 |
completed | March 12, 2026, 6:51 a.m. |
Created at: March 8, 2026, 3:08 p.m.