Triple
T23131935
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Penza Oblast |
E577191
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Serdobsk
Serdobsk is a small industrial city in western Russia known for its historical architecture and location within Penza Oblast.
|
E1571494
|
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: Serdobsk | Statement: [Penza Oblast, hasCity, Serdobsk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Serdobsk Context triple: [Penza Oblast, hasCity, Serdobsk]
-
A.
Kljusev
Kljusev is the surname of Nikola Kljusev, a prominent Macedonian economist and the first Prime Minister of independent Macedonia.
-
B.
Grusinskaya
Grusinskaya is a fading but still celebrated Russian ballerina whose loneliness and vulnerability are central to the drama of the film "Grand Hotel."
-
C.
Dobrynin
Dobrynin is a Russian surname most prominently associated with Anatoly Dobrynin, the long-serving Soviet ambassador to the United States during the Cold War.
-
D.
Sertolovo
Sertolovo is a town in northwestern Russia that serves primarily as a residential and military community near Saint Petersburg.
-
E.
Ozersk
Ozersk is a closed Russian city in Chelyabinsk Oblast known for its nuclear industry and association with the Mayak production facility.
- 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: Serdobsk Triple: [Penza Oblast, hasCity, Serdobsk]
Generated description
Serdobsk is a small industrial city in western Russia known for its historical architecture and location within Penza Oblast.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Serdobsk Target entity description: Serdobsk is a small industrial city in western Russia known for its historical architecture and location within Penza Oblast.
-
A.
Kljusev
Kljusev is the surname of Nikola Kljusev, a prominent Macedonian economist and the first Prime Minister of independent Macedonia.
-
B.
Grusinskaya
Grusinskaya is a fading but still celebrated Russian ballerina whose loneliness and vulnerability are central to the drama of the film "Grand Hotel."
-
C.
Dobrynin
Dobrynin is a Russian surname most prominently associated with Anatoly Dobrynin, the long-serving Soviet ambassador to the United States during the Cold War.
-
D.
Sertolovo
Sertolovo is a town in northwestern Russia that serves primarily as a residential and military community near Saint Petersburg.
-
E.
Ozersk
Ozersk is a closed Russian city in Chelyabinsk Oblast known for its nuclear industry and association with the Mayak production facility.
- 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_69e245f7b0e481909c473ff4e6a54e2c |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18e88909881908c695cd7d39d380c |
completed | April 29, 2026, 4:52 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c23f7dccc8190aaba967e73cc6892 |
completed | May 19, 2026, 8:48 a.m. |
| NEDg | Description generation | batch_6a0c28ed782c8190a60cf2212a1e7861 |
completed | May 19, 2026, 9:10 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0c29bca4c08190a29d4204c75b333f |
completed | May 19, 2026, 9:13 a.m. |
Created at: April 17, 2026, 4 p.m.