Triple
T6787579
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Werder (Havel) |
E155848
|
entity |
| Predicate | twinTown |
P1072
|
FINISHED |
| Object |
Klimowitschi
Klimowitschi is a town in Belarus known in part for its international municipal partnership with Werder (Havel) in Germany.
|
E619369
|
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: Klimowitschi | Statement: [Werder (Havel), twinTown, Klimowitschi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Klimowitschi Context triple: [Werder (Havel), twinTown, Klimowitschi]
-
A.
Malinovsky
Malinovsky is a Russian surname most notably associated with Soviet military commander and Marshal of the Soviet Union Rodion Malinovsky.
-
B.
Chernyakhovsky
Chernyakhovsky is a Slavic surname most notably associated with Soviet General Ivan Chernyakhovsky, a prominent commander during World War II.
-
C.
Zyuzino
Zyuzino is a Moscow Metro station on the Big Circle Line serving the Zyuzino District in southern Moscow.
-
D.
Dzerzhinsk
Dzerzhinsk is a major industrial city in western Russia known for its large chemical manufacturing sector and associated environmental issues.
-
E.
Piotrovsky
Piotrovsky is a Russian surname most prominently associated with Mikhail Piotrovsky, the long-serving director of the State Hermitage Museum in Saint Petersburg.
- 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: Klimowitschi Triple: [Werder (Havel), twinTown, Klimowitschi]
Generated description
Klimowitschi is a town in Belarus known in part for its international municipal partnership with Werder (Havel) in Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Klimowitschi Target entity description: Klimowitschi is a town in Belarus known in part for its international municipal partnership with Werder (Havel) in Germany.
-
A.
Malinovsky
Malinovsky is a Russian surname most notably associated with Soviet military commander and Marshal of the Soviet Union Rodion Malinovsky.
-
B.
Chernyakhovsky
Chernyakhovsky is a Slavic surname most notably associated with Soviet General Ivan Chernyakhovsky, a prominent commander during World War II.
-
C.
Zyuzino
Zyuzino is a Moscow Metro station on the Big Circle Line serving the Zyuzino District in southern Moscow.
-
D.
Dzerzhinsk
Dzerzhinsk is a major industrial city in western Russia known for its large chemical manufacturing sector and associated environmental issues.
-
E.
Piotrovsky
Piotrovsky is a Russian surname most prominently associated with Mikhail Piotrovsky, the long-serving director of the State Hermitage Museum in Saint Petersburg.
- 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_69c6881770fc8190972b2906390380f5 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d2907d0081908291aad66048b8b1 |
completed | March 27, 2026, 6:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c71a871a84819098891f66c6e5b579 |
completed | March 28, 2026, 12:02 a.m. |
| NEDg | Description generation | batch_69c71b6b87d8819085e6ae122f042626 |
completed | March 28, 2026, 12:06 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c71c00f86c819099ef6ae0766e9f3a |
completed | March 28, 2026, 12:08 a.m. |
Created at: March 27, 2026, 2:14 p.m.