Triple
T23398848
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | KK Cibona |
E559441
|
entity |
| Predicate | formerName |
P65
|
FINISHED |
| Object |
Lokomotiva
Lokomotiva was the earlier name of a prominent Zagreb-based basketball club that later became known as KK Cibona, one of Croatia’s most successful teams.
|
E1583674
|
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: Lokomotiva | Statement: [KK Cibona, formerName, Lokomotiva]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lokomotiva Context triple: [KK Cibona, formerName, Lokomotiva]
-
A.
NK Lokomotiva
NK Lokomotiva is a professional football club from Zagreb, Croatia, that competes in the country’s top league.
-
B.
Lokomotiv
Lokomotiv is a Russian professional football club based in Moscow that competes in the Russian Premier League.
-
C.
Red Star Belgrade
Red Star Belgrade is a Serbian professional football club from Belgrade, historically one of the most successful and popular teams in the Balkans and a former European Cup winner.
-
D.
Dinamo
Dinamo is a Moscow Metro station named after the nearby Dynamo sports complex and stadium, serving passengers on the Zamoskvoretskaya Line.
-
E.
Dinamo
Dinamo is a professional football club based in Tirana, Albania, known for its historic success in the Albanian football 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: Lokomotiva Triple: [KK Cibona, formerName, Lokomotiva]
Generated description
Lokomotiva was the earlier name of a prominent Zagreb-based basketball club that later became known as KK Cibona, one of Croatia’s most successful teams.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lokomotiva Target entity description: Lokomotiva was the earlier name of a prominent Zagreb-based basketball club that later became known as KK Cibona, one of Croatia’s most successful teams.
-
A.
NK Lokomotiva
NK Lokomotiva is a professional football club from Zagreb, Croatia, that competes in the country’s top league.
-
B.
Lokomotiv
Lokomotiv is a Russian professional football club based in Moscow that competes in the Russian Premier League.
-
C.
Red Star Belgrade
Red Star Belgrade is a Serbian professional football club from Belgrade, historically one of the most successful and popular teams in the Balkans and a former European Cup winner.
-
D.
Dinamo
Dinamo is a Romanian professional football club based in Bucharest, known for its rich history and passionate fan base.
-
E.
Dinamo
Dinamo is a Moscow Metro station named after the nearby Dynamo sports complex and stadium, serving passengers on the Zamoskvoretskaya Line.
- 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_69e24549610c8190a069d6411ce5f661 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1a4ddcb9481909881c77458c59c83 |
completed | April 29, 2026, 6:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c5df194bc819084aa4de85966c68f |
completed | May 19, 2026, 12:56 p.m. |
| NEDg | Description generation | batch_6a0c5f52fe308190b87c53f9a9d915e0 |
completed | May 19, 2026, 1:02 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0c5ffbea40819088a5a01a3763c718 |
completed | May 19, 2026, 1:05 p.m. |
Created at: April 17, 2026, 5:37 p.m.