Triple
T7564689
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Moskovsky Rail Terminal |
E178882
|
entity |
| Predicate | connectsTo |
P845
|
FINISHED |
| Object | Samara |
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 | Statement: [Moskovsky Rail Terminal, connectsTo, Samara]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Samara Context triple: [Moskovsky Rail Terminal, connectsTo, Samara]
-
A.
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.
-
B.
Samara
Samara is a city in northwestern Nigeria that forms part of the urban area of Zaria in Kaduna State.
-
C.
Samara
chosen
Samara is a major Russian city on the Volga River known as an important industrial, cultural, and transportation hub.
-
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.
Kazan
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.
- 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_69c69f2f80288190b95cceb4da92ab2b |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f8fc6a408190abf363a29359e764 |
completed | March 27, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c9061d1be0819094109703ec4a99f7 |
completed | March 29, 2026, 10:59 a.m. |
Created at: March 27, 2026, 3:50 p.m.