Triple
T323373
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Moscow Metro |
E6462
|
entity |
| Predicate | notableStation |
P3858
|
FINISHED |
| Object |
Kievskaya
Kievskaya is a prominent Moscow Metro station complex known for its ornate, Ukrainian-themed architecture and role as a major transfer hub.
|
E51121
|
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: Kievskaya | Statement: [Moscow Metro, notableStation, Kievskaya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kievskaya Context triple: [Moscow Metro, notableStation, Kievskaya]
-
A.
Komsomolskaya
Komsomolskaya is one of Moscow Metro’s most famous and ornate stations, renowned for its grand Baroque-style decor and elaborate mosaics.
-
B.
Astapovo
Astapovo is a small Russian railway station village historically known as the place where the writer Leo Tolstoy died in 1910.
-
C.
Tsaritsyn
Tsaritsyn was the original name of the Russian city now known as Volgograd, a major industrial and historical center on the Volga River.
-
D.
Dnipro
Dnipro is one of Ukraine’s largest industrial and cultural centers, located on the Dnieper River in the central-eastern part of the country.
-
E.
Kryvyi Rih
Kryvyi Rih is a major industrial city in central Ukraine known for its extensive iron ore mining and steel production.
- 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: Kievskaya Triple: [Moscow Metro, notableStation, Kievskaya]
Generated description
Kievskaya is a prominent Moscow Metro station complex known for its ornate, Ukrainian-themed architecture and role as a major transfer hub.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kievskaya Target entity description: Kievskaya is a prominent Moscow Metro station complex known for its ornate, Ukrainian-themed architecture and role as a major transfer hub.
-
A.
Komsomolskaya
Komsomolskaya is one of Moscow Metro’s most famous and ornate stations, renowned for its grand Baroque-style decor and elaborate mosaics.
-
B.
Astapovo
Astapovo is a small Russian railway station village historically known as the place where the writer Leo Tolstoy died in 1910.
-
C.
Tsaritsyn
Tsaritsyn was the original name of the Russian city now known as Volgograd, a major industrial and historical center on the Volga River.
-
D.
Dnipro
Dnipro is one of Ukraine’s largest industrial and cultural centers, located on the Dnieper River in the central-eastern part of the country.
-
E.
Kryvyi Rih
Kryvyi Rih is a major industrial city in central Ukraine known for its extensive iron ore mining and steel production.
- 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_69a2e7933d6c8190bb2592ad13286ef2 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2ee028c488190ad0109510de2956d |
completed | Feb. 28, 2026, 1:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a413ee31fc81908cbda4c0737d1b33 |
completed | March 1, 2026, 10:24 a.m. |
| NEDg | Description generation | batch_69a414552e488190b02254f5c078eeb1 |
completed | March 1, 2026, 10:26 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a41485f4048190b47a78d05fa775b1 |
completed | March 1, 2026, 10:27 a.m. |
Created at: Feb. 28, 2026, 1:08 p.m.