Triple
T29710252
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | France vs Australia |
E751758
|
entity |
| Predicate | firstFranceGoalScorer |
P2695
|
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: [France vs Australia, firstFranceGoalScorer, Antoine Griezmann]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstFranceGoalScorer Context triple: [France vs Australia, firstFranceGoalScorer, Antoine Griezmann]
-
A.
goalsScoredByFrance
Indicates the number of goals that were scored by the France team in a given match or event.
-
B.
goalsForFrance
Indicates that the subject scored a goal while playing for the France national team.
-
C.
finalGoalscorer
Indicates that an entity is the player who scored the last goal in a particular match or event.
-
D.
notableGoalScorer
Indicates that the subject is recognized for having scored a significant or noteworthy number of goals, typically in a sports context.
-
E.
goalScorer
chosen
Indicates that the subject is the player who scored a particular goal in a game or match.
- 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_69f0d62748848190b030d0a703629a7d |
completed | April 28, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_6a012594c27081908c80f0e0e6010290 |
completed | May 11, 2026, 12:40 a.m. |
| PD | Predicate disambiguation | batch_6a01252350548190b1df9edc9e581e91 |
completed | May 11, 2026, 12:38 a.m. |
Created at: April 28, 2026, 7:30 p.m.