Triple
T3156822
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Canada women's national ice hockey team |
E66003
|
entity |
| Predicate | 4NationsCupTitles |
P46668
|
FINISHED |
| Object | multiple titles |
—
|
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: multiple titles | Statement: [Canada women's national ice hockey team, 4NationsCupTitles, multiple titles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 4NationsCupTitles Context triple: [Canada women's national ice hockey team, 4NationsCupTitles, multiple titles]
-
A.
WorldCupOverallTitles
Indicates the total number of World Cup championship titles an entity has won across all tournaments.
-
B.
WorldCupVictories
Indicates the number of times an entity has won the FIFA World Cup tournament.
-
C.
ConfederationsCupTitles
Indicates the number of FIFA Confederations Cup championships an entity has won.
-
D.
WorldCupWins
Indicates the number of times an entity (typically a national team) has won the FIFA World Cup tournament.
-
E.
sharedMostWorldCupTitlesWith
Indicates that two entities hold the same highest number of World Cup titles compared to all other entities.
- 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_69ad85850c1481908a9e9c6242238de2 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada5eafa4c8190a65cc1312823144c |
completed | March 8, 2026, 4:38 p.m. |
| PD | Predicate disambiguation | batch_69ad9dfbf0348190952a6bca8fc5fed1 |
completed | March 8, 2026, 4:04 p.m. |
| PDg | Predicate description generation | batch_69ada1e4f7288190a80a1672e458132d |
completed | March 8, 2026, 4:20 p.m. |
Created at: March 8, 2026, 3:05 p.m.