Triple
T12483046
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2006 FIFA World Cup Golden Boot winner |
E298358
|
entity |
| Predicate | goalsScoredInKnockoutStageByWinner |
P9098
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [2006 FIFA World Cup Golden Boot winner, goalsScoredInKnockoutStageByWinner, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: goalsScoredInKnockoutStageByWinner Context triple: [2006 FIFA World Cup Golden Boot winner, goalsScoredInKnockoutStageByWinner, 1]
-
A.
pointsForShootoutWin
Indicates the number of points awarded to a team for winning a game via a shootout.
-
B.
penaltyShootoutWinner
Indicates that one competitor or team is the winner of a match decided by a penalty shootout.
-
C.
penaltyShootoutScore
Indicates the number of goals each side scored during a penalty shootout used to decide a tied match.
-
D.
numberOfGoals
chosen
Indicates the total count of goals scored or achieved by an entity in a given context.
-
E.
runnersUpGoalAverage
Indicates the average number of goals scored by entities that finished as runners-up in a competition or ranking.
- 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_69d6ada377208190a36011199a4d8558 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94e8a706c8190873623eab7db607d |
completed | April 10, 2026, 7:24 p.m. |
| PD | Predicate disambiguation | batch_69d94d41f3cc8190a3331fb9a895306f |
completed | April 10, 2026, 7:19 p.m. |
Created at: April 8, 2026, 9:56 p.m.