Triple
T8907889
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Derzhprom building |
E212106
|
entity |
| Predicate | architect |
P184
|
FINISHED |
| Object |
S. Kravets
S. Kravets was an architect known for contributing to the design of the constructivist Derzhprom building in Kharkiv, Ukraine.
|
E765639
|
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: S. Kravets | Statement: [Derzhprom building, architect, S. Kravets]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: S. Kravets Context triple: [Derzhprom building, architect, S. Kravets]
-
A.
Michael Starobin
Michael Starobin is a Tony Award–winning American orchestrator and arranger known for his work on numerous Broadway musicals.
-
B.
Mike Sokolsky
Mike Sokolsky is a co-founder of the online education platform Udacity, known for its technology-focused courses and nanodegree programs.
-
C.
Alec Miloslavsky
Alec Miloslavsky is a technology entrepreneur best known as a co-founder of the customer experience and contact center software company Genesys.
-
D.
V. Volodarsky
V. Volodarsky was a Russian Bolshevik revolutionary and political activist prominent in the early Soviet period.
-
E.
Max Zaritsky
Max Zaritsky was an American labor leader and union organizer who played a key role in the early development of industrial unionism in the United States.
- 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: S. Kravets Triple: [Derzhprom building, architect, S. Kravets]
Generated description
S. Kravets was an architect known for contributing to the design of the constructivist Derzhprom building in Kharkiv, Ukraine.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: S. Kravets Target entity description: S. Kravets was an architect known for contributing to the design of the constructivist Derzhprom building in Kharkiv, Ukraine.
-
A.
Michael Starobin
Michael Starobin is a Tony Award–winning American orchestrator and arranger known for his work on numerous Broadway musicals.
-
B.
Mike Sokolsky
Mike Sokolsky is a co-founder of the online education platform Udacity, known for its technology-focused courses and nanodegree programs.
-
C.
Alec Miloslavsky
Alec Miloslavsky is a technology entrepreneur best known as a co-founder of the customer experience and contact center software company Genesys.
-
D.
V. Volodarsky
V. Volodarsky was a Russian Bolshevik revolutionary and political activist prominent in the early Soviet period.
-
E.
Max Zaritsky
Max Zaritsky was an American labor leader and union organizer who played a key role in the early development of industrial unionism in the United States.
- 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_69ca839255248190b43984294abd92ae |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc64c6a87c81909331a39619f913c0 |
completed | April 1, 2026, 12:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfba31fc148190a8dbe378694dcc32 |
completed | April 3, 2026, 1:01 p.m. |
| NEDg | Description generation | batch_69cfbabf33a08190a18d13b9078c00e2 |
completed | April 3, 2026, 1:03 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cfbba71a948190afc03a1df9e5777c |
completed | April 3, 2026, 1:07 p.m. |
Created at: March 30, 2026, 6:55 p.m.