Triple
T19726002
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Severobaikalsk |
E473727
|
entity |
| Predicate | railConnection |
P848
|
FINISHED |
| Object |
Nizhneangarsk
Nizhneangarsk is a small urban locality in the Republic of Buryatia, Russia, situated at the northern tip of Lake Baikal and serving as a regional transport and administrative center.
|
E1399621
|
NE FINISHED |
How this triple was built (4 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: Nizhneangarsk | Statement: [Severobaikalsk, railConnection, Nizhneangarsk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nizhneangarsk Context triple: [Severobaikalsk, railConnection, Nizhneangarsk]
-
A.
Yuzhnouralsk
Yuzhnouralsk is a small industrial city in Russia’s Ural region, known for its energy and manufacturing enterprises.
-
B.
Zheleznogorsk-Ilimsky
Zheleznogorsk-Ilimsky is a small industrial town in Russia known for its mining and forestry-related industries.
-
C.
Kuznetsk
Kuznetsk is a city in Penza Oblast, Russia, known as an industrial and transport center in the Volga region.
-
D.
Nefteyugansk
Nefteyugansk is a major oil-producing city in western Siberia, Russia, known for its central role in the country’s petroleum industry.
-
E.
Novokuznetskaya
Novokuznetskaya is a Moscow Metro station known for its distinctive Stalinist architecture and richly decorated interiors.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Nizhneangarsk Triple: [Severobaikalsk, railConnection, Nizhneangarsk]
Generated description
Nizhneangarsk is a small urban locality in the Republic of Buryatia, Russia, situated at the northern tip of Lake Baikal and serving as a regional transport and administrative center.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nizhneangarsk Target entity description: Nizhneangarsk is a small urban locality in the Republic of Buryatia, Russia, situated at the northern tip of Lake Baikal and serving as a regional transport and administrative center.
-
A.
Yuzhnouralsk
Yuzhnouralsk is a small industrial city in Russia’s Ural region, known for its energy and manufacturing enterprises.
-
B.
Zheleznogorsk-Ilimsky
Zheleznogorsk-Ilimsky is a small industrial town in Russia known for its mining and forestry-related industries.
-
C.
Kuznetsk
Kuznetsk is a city in Penza Oblast, Russia, known as an industrial and transport center in the Volga region.
-
D.
Nefteyugansk
Nefteyugansk is a major oil-producing city in western Siberia, Russia, known for its central role in the country’s petroleum industry.
-
E.
Novokuznetskaya
Novokuznetskaya is a Moscow Metro station known for its distinctive Stalinist architecture and richly decorated interiors.
- F. None of above. chosen
Provenance (5 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_69d8e517ebd48190979ee76723bcfadf |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e649f7bedc81908f784832c0fc10a1 |
completed | April 20, 2026, 3:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a07dbb112208190bbed3f6ccf5494fa |
completed | May 16, 2026, 2:51 a.m. |
| NEDg | Description generation | batch_6a07dd176ce88190807d1515008c3c7c |
completed | May 16, 2026, 2:57 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a07dd98bba081908a2a2089a2e9947c |
completed | May 16, 2026, 2:59 a.m. |
Created at: April 10, 2026, 1:46 p.m.