Triple
T1257578
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Czech Republic men's national ice hockey team |
E12427
|
entity |
| Predicate | worldCupOfHockeyBestResult |
P24946
|
FINISHED |
| Object | runner-up |
—
|
LITERAL 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: runner-up | Statement: [Czech Republic men's national ice hockey team, worldCupOfHockeyBestResult, runner-up]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: worldCupOfHockeyBestResult Context triple: [Czech Republic men's national ice hockey team, worldCupOfHockeyBestResult, runner-up]
-
A.
worldCupOfHockeyTitles
Indicates the number of World Cup of Hockey championship titles an entity has won.
-
B.
WorldCupOfHockeyWinner
Indicates the team that won the specified edition of the World Cup of Hockey.
-
C.
olympicIceHockeyGoldMedals
Indicates the number of Olympic gold medals won in ice hockey by an entity (typically a team or country).
-
D.
WorldCupOfHockeyParticipant
Indicates that an entity took part as a team or competitor in a World Cup of Hockey tournament.
-
E.
bestWorldCupResult
Indicates the most successful performance or highest achievement an entity has attained in any FIFA World Cup tournament.
- F. None of above. chosen
Provenance (4 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_69a4933352e08190ac617291985e76c0 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4bfaa2b508190a3f61c67b3fa3ad4 |
completed | March 1, 2026, 10:37 p.m. |
| PD | Predicate disambiguation | batch_69a4bb6c977c8190a2bf3e8b67a59beb |
completed | March 1, 2026, 10:19 p.m. |
| PDg | Predicate description generation | batch_69a4bc4a1f048190bd1ddcc4cc3fe057 |
completed | March 1, 2026, 10:23 p.m. |
Created at: March 1, 2026, 7:50 p.m.