Triple
T36676219
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2014 IIHF World Championship |
E905547
|
entity |
| Predicate | venue |
P373
|
FINISHED |
| Object |
Minsk Arena
Minsk Arena is a large multi-purpose indoor arena in Minsk, Belarus, best known as a premier venue for international ice hockey and major entertainment events.
|
E2193440
|
NE FINISHED |
How this triple was built (2 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: Minsk Arena | Statement: [2014 IIHF World Championship, venue, Minsk Arena]
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: Minsk Arena Triple: [2014 IIHF World Championship, venue, Minsk Arena]
Generated description
Minsk Arena is a large multi-purpose indoor arena in Minsk, Belarus, best known as a premier venue for international ice hockey and major entertainment events.
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_69f76e7011dc819082b324f18b756a1b |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69f7c7a311588190928d93aa1eab4d7e |
completed | May 3, 2026, 10:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a3a20dba7288190bf78c0d75ff9ce6b |
completed | June 23, 2026, 5:59 a.m. |
| NEDg | Description generation | batch_6a3a21d045cc81908d61119fb621f7b6 |
completed | June 23, 2026, 6:04 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a3a225b99788190bafbd7ccb4a652b9 |
completed | June 23, 2026, 6:06 a.m. |
Created at: May 3, 2026, 4:12 p.m.