Triple
T23448218
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ferenc Puskás |
E565599
|
entity |
| Predicate | internationalGoals |
P43239
|
FINISHED |
| Object | Hungary: 84 |
—
|
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: Hungary: 84 | Statement: [Ferenc Puskás, internationalGoals, Hungary: 84]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: internationalGoals Context triple: [Ferenc Puskás, internationalGoals, Hungary: 84]
-
A.
goalsForNationalTeam
chosen
Indicates the number of goals an individual has scored while playing for their national team.
-
B.
totalInternationalGoals
Indicates the total number of goals an entity has scored in official international matches.
-
C.
worldCupGoals
Indicates the number of goals an entity scored in World Cup matches.
-
D.
internationalGoalsPerGameRatio
Indicates the ratio between the number of goals an entity scores in international matches and the number of international games it plays.
-
E.
FIFAWorldCupGoalsRank
Indicates the ranking of entities based on the number of goals they have scored in FIFA World Cup competitions.
- 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_69e24584f9488190bb32730bd2ce023e |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f1a64b27988190b4722425da964407 |
completed | April 29, 2026, 6:33 a.m. |
| PD | Predicate disambiguation | batch_69f06201d33481909b5fd7b92d03e658 |
completed | April 28, 2026, 7:30 a.m. |
Created at: April 17, 2026, 5:52 p.m.