Triple
T8583794
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | France vs Croatia (2018 FIFA World Cup Final) |
E203250
|
entity |
| Predicate | goalScorerFrance |
P2695
|
FINISHED |
| Object | Mario Mandžukić (own goal) |
—
|
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: Mario Mandžukić (own goal) | Statement: [France vs Croatia (2018 FIFA World Cup Final), goalScorerFrance, Mario Mandžukić (own goal)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: goalScorerFrance Context triple: [France vs Croatia (2018 FIFA World Cup Final), goalScorerFrance, Mario Mandžukić (own goal)]
-
A.
goalsForFrance
Indicates that the subject scored a goal while playing for the France national team.
-
B.
goalScorer
chosen
Indicates that the subject is the player who scored a particular goal in a game or match.
-
C.
goalScorerTeam
Indicates that a team is the one for which a particular goal scorer scored a goal.
-
D.
hasWonTopFlightLeagueInFrance
Indicates that the subject has won the championship title of the highest professional football league in France at least once.
-
E.
topGoalScorerGoals
Indicates the number of goals scored by the top goal scorer in a given context or competition.
- 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_69ca8329bb7c8190a63c643730839103 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbeb1ecc188190bbe2ab8d2423505b |
completed | March 31, 2026, 3:41 p.m. |
| PD | Predicate disambiguation | batch_69cbd11b13108190b07f8f161425a585 |
completed | March 31, 2026, 1:50 p.m. |
Created at: March 30, 2026, 6:22 p.m.