Triple
T2797846
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miroslav Klose |
E53080
|
entity |
| Predicate | worldCupRecord |
P43244
|
FINISHED |
| Object | FIFA World Cup all-time top scorer |
—
|
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: FIFA World Cup all-time top scorer | Statement: [Miroslav Klose, worldCupRecord, FIFA World Cup all-time top scorer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: worldCupRecord Context triple: [Miroslav Klose, worldCupRecord, FIFA World Cup all-time top scorer]
-
A.
WorldCupVictories
Indicates the number of times an entity has won the FIFA World Cup tournament.
-
B.
WorldCupOverallTitles
Indicates the total number of World Cup championship titles an entity has won across all tournaments.
-
C.
WorldCupWins
Indicates the number of times an entity (typically a national team) has won the FIFA World Cup tournament.
-
D.
worldCupAppearances
Indicates the number of times an entity has participated in a FIFA World Cup tournament.
-
E.
WorldCupAppearances
Indicates the number of times an entity has participated in FIFA World Cup final tournaments.
- F. None of above. chosen
Provenance (4 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_69ab495a90788190941b6917e1eca3a6 |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abddf204148190a53f3f30d645d94c |
completed | March 7, 2026, 8:12 a.m. |
| PD | Predicate disambiguation | batch_69abdd040f9481908e9c7a2df88ea1ae |
completed | March 7, 2026, 8:08 a.m. |
| PDg | Predicate description generation | batch_69abddcc348081908b5f760899389d4f |
completed | March 7, 2026, 8:11 a.m. |
Created at: March 6, 2026, 9:58 p.m.