Triple
T2430059
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1958 FIFA World Cup |
E52820
|
entity |
| Predicate | championTitleCountForBrazil |
P31745
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [1958 FIFA World Cup, championTitleCountForBrazil, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: championTitleCountForBrazil Context triple: [1958 FIFA World Cup, championTitleCountForBrazil, 1]
-
A.
winnerTitleCount
Indicates the number of titles or championships an entity has won.
-
B.
hasChampionships
Indicates that one entity possesses or has won one or more championships associated with another entity.
-
C.
WorldCupOverallTitles
chosen
Indicates the total number of World Cup championship titles an entity has won across all tournaments.
-
D.
championPreviousTitleYear
Indicates the year in which the current champion previously held the same title.
-
E.
championTitleCountSinceMoveToSanFrancisco
Indicates the number of championship titles an entity has won since relocating to San Francisco.
- 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_69ab4959bcc0819083246f9fb10439e3 |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abcc74a5108190a3a9631b0cc1a127 |
completed | March 7, 2026, 6:57 a.m. |
| PD | Predicate disambiguation | batch_69abc5aa1b60819081b87f7985c6cff3 |
completed | March 7, 2026, 6:28 a.m. |
Created at: March 6, 2026, 9:43 p.m.