Triple
T30377850
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | George Gipp |
E772739
|
entity |
| Predicate | rushingYardsRecord |
P169133
|
FINISHED |
| Object | held Notre Dame career rushing record for several decades |
—
|
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: held Notre Dame career rushing record for several decades | Statement: [George Gipp, rushingYardsRecord, held Notre Dame career rushing record for several decades]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rushingYardsRecord Context triple: [George Gipp, rushingYardsRecord, held Notre Dame career rushing record for several decades]
-
A.
rushingYardsLeaderSeasons
Indicates the seasons in which a given player led a league or competition in total rushing yards.
-
B.
rushingYards
Indicates the number of yards a player gains by running the ball on rushing plays.
-
C.
NFLAllTimeRushingYardsLeader
Indicates that the subject is the player who has accumulated the most career rushing yards in NFL history.
-
D.
rushingYardsWithTeam
Indicates the number of rushing yards a player gained while playing for a specific team.
-
E.
singleSeasonRushingYardsRecord
Indicates the record-setting total number of rushing yards accumulated by a player in a single season.
- 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_69f2248e3444819081b05712dc6873de |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f68515aa2081908bae3de1802bd9df |
completed | May 2, 2026, 11:13 p.m. |
| PD | Predicate disambiguation | batch_69f678d019fc8190913662cd2f87b857 |
completed | May 2, 2026, 10:21 p.m. |
| PDg | Predicate description generation | batch_69f679496c188190ba585792f987a1f4 |
completed | May 2, 2026, 10:23 p.m. |
Created at: April 29, 2026, 8 p.m.