Triple
T6852244
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ken Anderson |
E158046
|
entity |
| Predicate | statisticPassingYards |
P17446
|
FINISHED |
| Object | over 32,000 |
—
|
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: over 32,000 | Statement: [Ken Anderson, statisticPassingYards, over 32,000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: statisticPassingYards Context triple: [Ken Anderson, statisticPassingYards, over 32,000]
-
A.
passingYardsCareer
chosen
Indicates the total number of yards a player has gained by passing the ball over the course of their entire career.
-
B.
ledNFLInPassingYards
Indicates that the subject was the league leader in total passing yards in the NFL for a given season.
-
C.
passingTouchdownsCareer
Indicates the total number of touchdown passes a player has thrown over the course of their entire career.
-
D.
careerReceivingYards
Indicates the total number of yards a player has gained by receiving the ball over the course of their entire career.
-
E.
rushingYards
Indicates the number of yards a player gains by running the ball on rushing plays.
- 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_69c6882fae988190864cbba788c5ebb4 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d84e6fb08190915954be5b0df2d8 |
completed | March 27, 2026, 7:19 p.m. |
| PD | Predicate disambiguation | batch_69c6d0a12834819097d7e6c0b823745e |
completed | March 27, 2026, 6:46 p.m. |
Created at: March 27, 2026, 2:20 p.m.