Triple
T18246125
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Government of Latvia |
E436957
|
entity |
| Predicate | currentHeadOfGovernment |
P307
|
FINISHED |
| Object |
Evika Siliņa
Evika Siliņa is a Latvian politician who serves as the country’s prime minister.
|
E1313454
|
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: Evika Siliņa | Statement: [Government of Latvia, currentHeadOfGovernment, Evika Siliņa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Evika Siliņa Context triple: [Government of Latvia, currentHeadOfGovernment, Evika Siliņa]
-
A.
Iraida Jansone
Iraida Jansone was the mother of renowned Latvian conductor Mariss Jansons.
-
B.
Anna-Marija Slotiņa
Anna-Marija Slotiņa is a Latvian actress and former model best known publicly as the wife of Chilean-British actor Santiago Cabrera.
-
C.
Melanija Knavs
Melanija Knavs is the Slovenian-born former fashion model who became First Lady of the United States as the wife of Donald Trump.
-
D.
Tonia Toumanova
Tonia Toumanova is a central female character in Nikolai Ostrovsky’s socialist realist novel "How the Steel Was Tempered," representing romantic and ideological conflict in the life of protagonist Pavel Korchagin.
-
E.
Anfisa Vistingauzen
Anfisa Vistingauzen is a Russian actress known for her roles in film and television, including the thriller "Metro."
- 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: Evika Siliņa Triple: [Government of Latvia, currentHeadOfGovernment, Evika Siliņa]
Generated description
Evika Siliņa is a Latvian politician who serves as the country’s prime minister.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Evika Siliņa Target entity description: Evika Siliņa is a Latvian politician who serves as the country’s prime minister.
-
A.
Iraida Jansone
Iraida Jansone was the mother of renowned Latvian conductor Mariss Jansons.
-
B.
Anna-Marija Slotiņa
Anna-Marija Slotiņa is a Latvian actress and former model best known publicly as the wife of Chilean-British actor Santiago Cabrera.
-
C.
Melanija Knavs
Melanija Knavs is the Slovenian-born former fashion model who became First Lady of the United States as the wife of Donald Trump.
-
D.
Tonia Toumanova
Tonia Toumanova is a central female character in Nikolai Ostrovsky’s socialist realist novel "How the Steel Was Tempered," representing romantic and ideological conflict in the life of protagonist Pavel Korchagin.
-
E.
Anfisa Vistingauzen
Anfisa Vistingauzen is a Russian actress known for her roles in film and television, including the thriller "Metro."
- 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_69d8b91104e08190a8241f7d260a5162 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4f7e7d49c8190b227a13b63615754 |
completed | April 19, 2026, 3:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a03ac733e708190a7fde1fb61db5d5f |
completed | May 12, 2026, 10:40 p.m. |
| NEDg | Description generation | batch_6a03ad2be6e0819081426da968a57db0 |
completed | May 12, 2026, 10:43 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a03ad8898748190b57028bb2e2ed207 |
completed | May 12, 2026, 10:45 p.m. |
Created at: April 10, 2026, 10:33 a.m.