Triple
T838425
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Valeri Kamensky |
E18122
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object |
Voskresensk
Voskresensk is a town in Moscow Oblast, Russia, known for its industrial base and strong ice hockey tradition.
|
E138871
|
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: Voskresensk | Statement: [Valeri Kamensky, placeOfBirth, Voskresensk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Voskresensk Context triple: [Valeri Kamensky, placeOfBirth, Voskresensk]
-
A.
Kievskaya
Kievskaya is a prominent Moscow Metro station complex known for its ornate, Ukrainian-themed architecture and role as a major transfer hub.
-
B.
Kolomna
Kolomna is a historic Russian city southeast of Moscow, known for its well-preserved kremlin, medieval architecture, and traditional pastila confectionery.
-
C.
Novoslobodskaya
Novoslobodskaya is a Moscow Metro station famed for its distinctive stained-glass panels and ornate, cathedral-like interior design.
-
D.
Odintsovo
Odintsovo is a town in western Russia that serves as an important suburban center just outside Moscow.
-
E.
Astapovo
Astapovo is a small Russian railway station village historically known as the place where the writer Leo Tolstoy died in 1910.
- 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: Voskresensk Triple: [Valeri Kamensky, placeOfBirth, Voskresensk]
Generated description
Voskresensk is a town in Moscow Oblast, Russia, known for its industrial base and strong ice hockey tradition.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Voskresensk Target entity description: Voskresensk is a town in Moscow Oblast, Russia, known for its industrial base and strong ice hockey tradition.
-
A.
Kievskaya
Kievskaya is a prominent Moscow Metro station complex known for its ornate, Ukrainian-themed architecture and role as a major transfer hub.
-
B.
Kolomna
Kolomna is a historic Russian city southeast of Moscow, known for its well-preserved kremlin, medieval architecture, and traditional pastila confectionery.
-
C.
Novoslobodskaya
Novoslobodskaya is a Moscow Metro station famed for its distinctive stained-glass panels and ornate, cathedral-like interior design.
-
D.
Odintsovo
Odintsovo is a town in western Russia that serves as an important suburban center just outside Moscow.
-
E.
Astapovo
Astapovo is a small Russian railway station village historically known as the place where the writer Leo Tolstoy died in 1910.
- 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_69a49389f44881909a608fb27d89f247 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4abd0e8bc8190afe29cd4745c2f86 |
completed | March 1, 2026, 9:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac82f25c088190ae32593e6edbb8d0 |
completed | March 7, 2026, 7:56 p.m. |
| NEDg | Description generation | batch_69ac83c99fe481909b62ba635c4bc98c |
completed | March 7, 2026, 8 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ac843a0bdc819099ee4ae275ef5669 |
completed | March 7, 2026, 8:02 p.m. |
Created at: March 1, 2026, 7:38 p.m.