Triple
T15439644
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Barry Switzer |
E369862
|
entity |
| Predicate | overallNFLRecord |
P44267
|
FINISHED |
| Object | 40–24 |
—
|
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: 40–24 | Statement: [Barry Switzer, overallNFLRecord, 40–24]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: overallNFLRecord Context triple: [Barry Switzer, overallNFLRecord, 40–24]
-
A.
nflRecord
chosen
Indicates the win-loss-tie performance record a team or individual has accumulated in NFL competition.
-
B.
NFLRecordHeld
Indicates that one entity holds or has held a specific NFL record associated with another entity (such as a statistic, category, or achievement).
-
C.
NFLTeamAchievement
Indicates that an NFL team has attained a specific accomplishment, milestone, or honor within professional football competition.
-
D.
coachingRecordNFLWins
Indicates the number of NFL games a coach has won in their coaching career.
-
E.
nflAllTimePointsLeader
Indicates that the subject is the player who holds the record for the most career points scored in NFL history.
- 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_69d85a19180081909925012fbf4e62a3 |
completed | April 10, 2026, 2:02 a.m. |
| NER | Named-entity recognition | batch_69e03eddf258819082679970b7d2b6af |
completed | April 16, 2026, 1:43 a.m. |
| PD | Predicate disambiguation | batch_69ded28276f481908c2038bb301e57cf |
completed | April 14, 2026, 11:49 p.m. |
Created at: April 10, 2026, 3:21 a.m.