Triple
T6648984
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Just Fontaine |
E150770
|
entity |
| Predicate | clubNumberOfGoalsForStadeDeReims |
P9098
|
FINISHED |
| Object | over 100 goals |
—
|
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: over 100 goals | Statement: [Just Fontaine, clubNumberOfGoalsForStadeDeReims, over 100 goals]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: clubNumberOfGoalsForStadeDeReims Context triple: [Just Fontaine, clubNumberOfGoalsForStadeDeReims, over 100 goals]
-
A.
goalScorerTeam
Indicates that a team is the one for which a particular goal scorer scored a goal.
-
B.
numberOfGoals
chosen
Indicates the total count of goals scored or achieved by an entity in a given context.
-
C.
goalsForFrance
Indicates that the subject scored a goal while playing for the France national team.
-
D.
totalGoalsRecord
Indicates the total number of goals that have been recorded for an entity across all relevant events or contexts.
-
E.
clubGoalsForInterMilan
Indicates the total number of goals a player has scored for Inter Milan at club level.
- 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_69c687f1a3048190828b7342f7125d5c |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6cc9c6cb0819084fec8e0beb430de |
completed | March 27, 2026, 6:29 p.m. |
| PD | Predicate disambiguation | batch_69c6ad04d66c8190926ffcbff372643b |
completed | March 27, 2026, 4:15 p.m. |
Created at: March 27, 2026, 2:01 p.m.