Triple
T4632382
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Super Bowl XXXIV |
E101445
|
entity |
| Predicate | gameWinningPointsType |
P2633
|
FINISHED |
| Object | field goal and touchdown combination |
—
|
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: field goal and touchdown combination | Statement: [Super Bowl XXXIV, gameWinningPointsType, field goal and touchdown combination]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: gameWinningPointsType Context triple: [Super Bowl XXXIV, gameWinningPointsType, field goal and touchdown combination]
-
A.
gameWinningScoreType
chosen
Indicates the type or category of score (e.g., goal, point, run) that results in winning a game.
-
B.
gameWinningScoreBy
Indicates that a particular score is the decisive amount by which a game is won by an entity.
-
C.
gameWinningScore
Indicates that a particular score results in winning the game.
-
D.
pointsForWin
Indicates the number of points awarded to an entity for achieving a win in a given context or competition.
-
E.
pointsToWinSet
Indicates the number of points a player or side must win to secure the current set.
- 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_69bd43d2f1c081908cd4b7ec48ecc73d |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd5a342ffc8190a911d0598ed230bb |
completed | March 20, 2026, 2:31 p.m. |
| PD | Predicate disambiguation | batch_69bd5233cb5081908807e2b150f0ca06 |
completed | March 20, 2026, 1:57 p.m. |
Created at: March 20, 2026, 1:13 p.m.