Triple
T10642796
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oral |
E250763
|
entity |
| Predicate | roadConnectedTo |
P11435
|
FINISHED |
| Object | Samara (Russia) |
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 (Russia) | Statement: [Oral, roadConnectedTo, Samara (Russia)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Samara (Russia) Context triple: [Oral, roadConnectedTo, Samara (Russia)]
-
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.
Samara
Samara is a city in northwestern Nigeria that forms part of the urban area of Zaria in Kaduna State.
-
D.
Lesosibirsk
Lesosibirsk is a town in central Siberia, Russia, known historically as a major timber-processing and river port center on the Yenisei River.
-
E.
Ekaterinodar
Ekaterinodar, now known as Krasnodar, was a major city in southern Russia that served as an important political and military center in the Kuban region.
- 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_69d6aa5a4c4881908f39be6efe5981e5 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6dfcf65fc81909a0c86daefaab1ab |
completed | April 8, 2026, 11:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e2161018408190bcb64efba0974f8c |
completed | April 17, 2026, 11:14 a.m. |
Created at: April 8, 2026, 9:05 p.m.