Triple
T35944609
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FIFA World Youth Championship 1999 matches |
E1039553
|
entity |
| Predicate | goalsCountedFor |
P108963
|
FINISHED |
| Object | 1999 FIFA World Youth Championship statistics |
—
|
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: 1999 FIFA World Youth Championship statistics | Statement: [FIFA World Youth Championship 1999 matches, goalsCountedFor, 1999 FIFA World Youth Championship statistics]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: goalsCountedFor Context triple: [FIFA World Youth Championship 1999 matches, goalsCountedFor, 1999 FIFA World Youth Championship statistics]
-
A.
numberOfGoals
Indicates the total count of goals scored or achieved by an entity in a given context.
-
B.
scoredFor
Indicates that one entity achieved points or a score on behalf of another entity, such as a player scoring for a team.
-
C.
goalNumber
Indicates that an entity is associated with a specific target or objective quantified as a number.
-
D.
playerGoal
Indicates that a player scores, achieves, or is credited with a goal in a game or match.
-
E.
goalsFor
chosen
Indicates the number of goals scored by one participant or team in favor of a particular side or match context.
- 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_69f76e24bbd0819096b837d35371639a |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7ac23d1388190bdf9628b294943bd |
completed | May 3, 2026, 8:12 p.m. |
| PD | Predicate disambiguation | batch_69f7ab734d848190a84f9b8c3a952b75 |
completed | May 3, 2026, 8:09 p.m. |
Created at: May 3, 2026, 4:07 p.m.