Triple
T23598107
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Atlético de Madrid 2017–18 UEFA Europa League campaign |
E582673
|
entity |
| Predicate | finalGoalscorer |
P152867
|
FINISHED |
| Object | Antoine Griezmann |
—
|
NE NERFINISHED |
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: Antoine Griezmann | Statement: [Atlético de Madrid 2017–18 UEFA Europa League campaign, finalGoalscorer, Antoine Griezmann]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: finalGoalscorer Context triple: [Atlético de Madrid 2017–18 UEFA Europa League campaign, finalGoalscorer, Antoine Griezmann]
-
A.
goalScorer
Indicates that the subject is the player who scored a particular goal in a game or match.
-
B.
titleWinningGoalScorer
Indicates the player who scored the decisive goal that secured a title or championship in a competition.
-
C.
topGoalScorerGoals
Indicates the number of goals scored by the top goal scorer in a given context or competition.
-
D.
notableGoalScorer
Indicates that the subject is recognized for having scored a significant or noteworthy number of goals, typically in a sports context.
-
E.
seriesWinningGoalScorer
Indicates the player who scored the decisive goal that clinched victory in a multi-game series.
- 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_69e248f9e0a08190814772847003b1ff |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1b091992c819085f8aa6cb91cb76a |
completed | April 29, 2026, 7:17 a.m. |
| PD | Predicate disambiguation | batch_69f118c96a0081908a8ac98ef7e7e60c |
completed | April 28, 2026, 8:30 p.m. |
| PDg | Predicate description generation | batch_69f121cc494081908c987adfcde89b0e |
completed | April 28, 2026, 9:08 p.m. |
Created at: April 17, 2026, 6:43 p.m.