Triple
T5646456
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | David Garrard |
E124395
|
entity |
| Predicate | careerNFLPassingYards |
P17446
|
FINISHED |
| Object | 16603 passing yards (regular season) |
—
|
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: 16603 passing yards (regular season) | Statement: [David Garrard, careerNFLPassingYards, 16603 passing yards (regular season)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: careerNFLPassingYards Context triple: [David Garrard, careerNFLPassingYards, 16603 passing yards (regular season)]
-
A.
ledNFLInPassingYards
Indicates that the subject was the league leader in total passing yards in the NFL for a given season.
-
B.
careerReceivingYards
Indicates the total number of yards a player has gained by receiving the ball over the course of their entire career.
-
C.
ledNFLInPassingTouchdowns
Indicates that the subject was the league leader in passing touchdowns in the NFL for a given season or time period.
-
D.
careerRushingYards
Indicates the total number of rushing yards an entity has accumulated over the entire span of its career.
-
E.
passingYardsCareer
chosen
Indicates the total number of yards a player has gained by passing the ball over the course of their entire career.
- 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_69c00825df388190a58742fa9b1aa33d |
completed | March 22, 2026, 3:17 p.m. |
| NER | Named-entity recognition | batch_69c022abf0108190b10b3a9fe1688bf9 |
completed | March 22, 2026, 5:11 p.m. |
| PD | Predicate disambiguation | batch_69c01b2168508190b64b355cf50034ad |
completed | March 22, 2026, 4:38 p.m. |
Created at: March 22, 2026, 3:41 p.m.