Triple
T3904432
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1999 St. Louis Rams offense |
E90570
|
entity |
| Predicate | rushingYards |
P52812
|
FINISHED |
| Object | over 1900 |
—
|
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 1900 | Statement: [1999 St. Louis Rams offense, rushingYards, over 1900]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rushingYards Context triple: [1999 St. Louis Rams offense, rushingYards, over 1900]
-
A.
careerRushingYards
Indicates the total number of rushing yards an entity has accumulated over the entire span of its career.
-
B.
runningBack
Indicates that one entity is acting as a running back in relation to another entity, typically within the context of an American football play or team.
-
C.
passRusher
Indicates that an entity performs the role or action of rushing the passer, typically attempting to pressure or sack the quarterback.
-
D.
touchdownsScored
Indicates the number of touchdowns that an entity has scored.
-
E.
NFLAllTimeRushingYardsLeader
Indicates that the subject is the player who has accumulated the most career rushing yards in NFL history.
- F. None of above. chosen
Provenance (4 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_69aed95d315881908cbf1bf4a7215fbf |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aef1abe2dc81909c18aeae9b286898 |
completed | March 9, 2026, 4:13 p.m. |
| PD | Predicate disambiguation | batch_69aee75cff148190b6d5979d17fae085 |
completed | March 9, 2026, 3:29 p.m. |
| PDg | Predicate description generation | batch_69aef1aada308190821a3dfa6af170b3 |
completed | March 9, 2026, 4:13 p.m. |
Created at: March 9, 2026, 3:22 p.m.