Triple
T4722071
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Super Bowl X |
E104790
|
entity |
| Predicate | typeOfScoringPlay |
P55791
|
FINISHED |
| Object | touchdown |
—
|
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: touchdown | Statement: [Super Bowl X, typeOfScoringPlay, touchdown]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfScoringPlay Context triple: [Super Bowl X, typeOfScoringPlay, touchdown]
-
A.
scoredTouchdownType
chosen
Indicates the specific manner or category of touchdown that was scored in a scoring event.
-
B.
scoringType
Indicates the method or criteria by which performance, outcomes, or results are evaluated and assigned a score in a given context.
-
C.
notablePlayType
Indicates that a particular type or category of play is especially significant or characteristic for the subject.
-
D.
gameWinningScoreType
Indicates the type or category of score (e.g., goal, point, run) that results in winning a game.
-
E.
pointsScored
Indicates the number of points an entity has earned or achieved in a particular event, game, or context.
- 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_69bd43ed84648190ae0b7ee8e8d00482 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd67c9c3c08190a6c4944cdd1362a8 |
completed | March 20, 2026, 3:29 p.m. |
| PD | Predicate disambiguation | batch_69bd6220071881909670c89d072ffb6d |
completed | March 20, 2026, 3:05 p.m. |
Created at: March 20, 2026, 1:18 p.m.