Triple
T8087480
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2000 IIHF Women's World Championship |
E188768
|
entity |
| Predicate | averageGoalsPerGame |
P24386
|
FINISHED |
| Object | 6.6 |
—
|
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: 6.6 | Statement: [2000 IIHF Women's World Championship, averageGoalsPerGame, 6.6]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: averageGoalsPerGame Context triple: [2000 IIHF Women's World Championship, averageGoalsPerGame, 6.6]
-
A.
averageGoalsPerMatch
chosen
Indicates the typical number of goals scored per match in the context of the given entities or competition.
-
B.
internationalGoalsPerGameRatio
Indicates the ratio between the number of goals an entity scores in international matches and the number of international games it plays.
-
C.
usesGoalAverage
Indicates that one entity determines outcomes or rankings based on the average number of goals scored, typically in a sports or competitive context.
-
D.
numberOfGoals
Indicates the total count of goals scored or achieved by an entity in a given context.
-
E.
leagueGoalsAgainst
Indicates the number of goals a team has conceded in league competition against its opponents.
- F. None of above.
Provenance (3 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_69ca82b662e88190b9323daab8c28a21 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb4162244481908ab1202a9974deaa |
completed | March 31, 2026, 3:37 a.m. |
| PD | Predicate disambiguation | batch_69cb04a14cd88190a79ed26cbeec1c33 |
completed | March 30, 2026, 11:17 p.m. |
Created at: March 30, 2026, 5:29 p.m.