Triple
T15682745
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Uljana Semjonova |
E377618
|
entity |
| Predicate | memberOfSportsTeam |
P330
|
FINISHED |
| Object |
TTT Riga
TTT Riga is a historically dominant Latvian women’s basketball club renowned for its success in European competitions.
|
E1170815
|
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: TTT Riga | Statement: [Uljana Semjonova, memberOfSportsTeam, TTT Riga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TTT Riga Context triple: [Uljana Semjonova, memberOfSportsTeam, TTT Riga]
-
A.
Riga FC
Riga FC is a professional football club based in Riga, Latvia, competing in the Latvian Higher League.
-
B.
Dinamo Riga
Dinamo Riga is a professional ice hockey club based in Riga, Latvia, known for competing in top European and international leagues.
-
C.
CS Tiligul-Tiras Tiraspol
CS Tiligul-Tiras Tiraspol was a Moldovan football club based in Tiraspol that competed in the country’s top division after the Soviet era.
-
D.
TPS Turku
TPS Turku is a Finnish professional ice hockey club based in Turku, known as one of the country’s most successful and historic teams.
-
E.
FK Liepāja
FK Liepāja is a professional football club from the Latvian city of Liepāja that competes in the country’s top-tier league.
- 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: TTT Riga Triple: [Uljana Semjonova, memberOfSportsTeam, TTT Riga]
Generated description
TTT Riga is a historically dominant Latvian women’s basketball club renowned for its success in European competitions.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: TTT Riga Target entity description: TTT Riga is a historically dominant Latvian women’s basketball club renowned for its success in European competitions.
-
A.
Riga FC
Riga FC is a professional football club based in Riga, Latvia, competing in the Latvian Higher League.
-
B.
Dinamo Riga
Dinamo Riga is a professional ice hockey club based in Riga, Latvia, known for competing in top European and international leagues.
-
C.
CS Tiligul-Tiras Tiraspol
CS Tiligul-Tiras Tiraspol was a Moldovan football club based in Tiraspol that competed in the country’s top division after the Soviet era.
-
D.
TPS Turku
TPS Turku is a Finnish professional ice hockey club based in Turku, known as one of the country’s most successful and historic teams.
-
E.
FK Liepāja
FK Liepāja is a professional football club from the Latvian city of Liepāja that competes in the country’s top-tier league.
- 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_69d85cd2e28481909d4e975bee20872f |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04f31b5b881908e46ecd9fc6048ab |
completed | April 16, 2026, 2:53 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff6ee4c8688190ae2fefb56171161a |
completed | May 9, 2026, 5:29 p.m. |
| NEDg | Description generation | batch_69ff705476008190b6151491bf89654e |
completed | May 9, 2026, 5:35 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff70ea739081909f63657c8fd6fa81 |
completed | May 9, 2026, 5:37 p.m. |
Created at: April 10, 2026, 4:16 a.m.