Triple
T36532399
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Herb Adderley |
E900479
|
entity |
| Predicate | touchdownsScoredOnInterceptions |
P41546
|
FINISHED |
| Object | 7 |
—
|
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: 7 | Statement: [Herb Adderley, touchdownsScoredOnInterceptions, 7]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: touchdownsScoredOnInterceptions Context triple: [Herb Adderley, touchdownsScoredOnInterceptions, 7]
-
A.
interceptionReturnTouchdowns
chosen
Indicates the number of times a defensive player returns an intercepted pass into the opponent’s end zone for a touchdown.
-
B.
touchdownsScored
Indicates the number of touchdowns that an entity has scored.
-
C.
tacklesForSafeties
Indicates that one entity performs a tackle on another entity that results in a safety (a scoring play where the ball carrier is downed in their own end zone).
-
D.
interceptionReturnYards
Indicates the number of yards gained by a defensive player while returning an intercepted pass.
-
E.
fumbleReturnTouchdowns
Indicates the number of touchdowns a player or team scores by recovering an opponent’s fumble and returning it to the end zone.
- 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_69f76e5fbb388190b70c4c15573c8143 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7c371931c8190afb1d4dd5157f92c |
completed | May 3, 2026, 9:51 p.m. |
| PD | Predicate disambiguation | batch_69f7c1b91fd88190ab85afd626603769 |
completed | May 3, 2026, 9:44 p.m. |
Created at: May 3, 2026, 4:11 p.m.