Triple
T821718
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roger Staubach |
E17761
|
entity |
| Predicate | NFLRushingYards |
P14715
|
FINISHED |
| Object | 2200+ |
—
|
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: 2200+ | Statement: [Roger Staubach, NFLRushingYards, 2200+]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: NFLRushingYards Context triple: [Roger Staubach, NFLRushingYards, 2200+]
-
A.
nflRushingYardsLeader
Indicates the player who gained the most rushing yards in the NFL over a specified season or time period.
-
B.
nflRushingTouchdownsLeader
Indicates the player who led all others in the number of rushing touchdowns in a given NFL season or context.
-
C.
ledNFLInPassingYards
Indicates that the subject was the league leader in total passing yards in the NFL for a given season.
-
D.
careerRushingYards
chosen
Indicates the total number of rushing yards an entity has accumulated over the entire span of its career.
-
E.
ledNFLInPassingTouchdowns
Indicates that the subject was the league leader in passing touchdowns in the NFL for a given season or time period.
- 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_69a4937bcaac8190a322524ac6f45a5a |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4ab976094819086d676404d745750 |
completed | March 1, 2026, 9:11 p.m. |
| PD | Predicate disambiguation | batch_69a4aa76a7808190ac7fd9ba1a4cebcb |
completed | March 1, 2026, 9:07 p.m. |
Created at: March 1, 2026, 7:38 p.m.