Triple
T8722216
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Fumble (1978 NFL play) |
E207037
|
entity |
| Predicate | winningScoreType |
P2633
|
FINISHED |
| Object | defensive 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: defensive touchdown | Statement: [The Fumble (1978 NFL play), winningScoreType, defensive touchdown]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: winningScoreType Context triple: [The Fumble (1978 NFL play), winningScoreType, defensive touchdown]
-
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.
scoringType
Indicates the method or criteria by which performance, outcomes, or results are evaluated and assigned a score in a given context.
-
E.
winnerType
Indicates the category or kind of winner associated with an event, competition, or outcome.
- 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_69ca835811d8819081ea00fd2a2c9a1c |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5d0609f48190adc56226724b16c6 |
completed | March 31, 2026, 11:47 p.m. |
| PD | Predicate disambiguation | batch_69cc457093188190959287a6458651c6 |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:36 p.m.