Triple
T10067094
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lawrence Tynes |
E213127
|
entity |
| Predicate | scoredOvertimeGameWinner |
P13630
|
FINISHED |
| Object | 2007 NFC Championship Game vs Green Bay Packers |
—
|
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: 2007 NFC Championship Game vs Green Bay Packers | Statement: [Lawrence Tynes, scoredOvertimeGameWinner, 2007 NFC Championship Game vs Green Bay Packers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: scoredOvertimeGameWinner Context triple: [Lawrence Tynes, scoredOvertimeGameWinner, 2007 NFC Championship Game vs Green Bay Packers]
-
A.
scoredGameWinningGoal
Indicates that an entity scored the decisive goal that determined the final victory in a game.
-
B.
championshipGameScore
Indicates the final score achieved by each participant in a championship game.
-
C.
pointsForOvertimeOrShootoutLoss
Indicates the number of points awarded to a team specifically for losing a game in overtime or a shootout.
-
D.
decisiveTouchdownScorer
chosen
Indicates the player who scored the touchdown that ultimately determined the outcome of the game.
-
E.
winningTeamScore
Indicates the number of points or goals achieved by the team that wins a particular game or competition.
- 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_69ca83977128819084084eb7d1d8c52a |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cdcff63a4c8190bb08a0428aafa189 |
completed | April 2, 2026, 2:09 a.m. |
| PD | Predicate disambiguation | batch_69cd4b92573481909389bc6148ae7ea8 |
completed | April 1, 2026, 4:45 p.m. |
Created at: March 30, 2026, 8:58 p.m.