Triple
T21629753
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2010 FIFA World Cup final |
E533796
|
entity |
| Predicate | numberOfYellowCards |
P145299
|
FINISHED |
| Object | 14 |
—
|
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: 14 | Statement: [2010 FIFA World Cup final, numberOfYellowCards, 14]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfYellowCards Context triple: [2010 FIFA World Cup final, numberOfYellowCards, 14]
-
A.
redCardInWorldCupFinal
Indicates that a player was shown a red card during a World Cup final match.
-
B.
numberOfGoals
Indicates the total count of goals scored or achieved by an entity in a given context.
-
C.
refereeSignal
Indicates that a referee communicates a decision or instruction through an official signal (e.g., whistle, gesture, or flag) regarding an event in the game.
-
D.
penaltyScoredBy
Indicates that a penalty (typically in a game or sport) was successfully converted or scored by a particular participant.
-
E.
numberOfEnglandGoals
Indicates the number of goals scored by the England team in a given match or context.
- 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_69e0c464fba881908d0ff2ac80511ce1 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef5215ae3c81909e6dedba23822970 |
completed | April 27, 2026, 12:09 p.m. |
| PD | Predicate disambiguation | batch_69e69677b9c48190bf81f795aa8ad74e |
completed | April 20, 2026, 9:11 p.m. |
| PDg | Predicate description generation | batch_69e69cb4bcbc8190a4fc2d508df107be |
completed | April 20, 2026, 9:37 p.m. |
Created at: April 16, 2026, 6:34 p.m.