Triple
T7212242
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Warren Moon |
E149431
|
entity |
| Predicate | CFLCareerPassingYards |
P17446
|
FINISHED |
| Object | over 21,000 |
—
|
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 21,000 | Statement: [Warren Moon, CFLCareerPassingYards, over 21,000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: CFLCareerPassingYards Context triple: [Warren Moon, CFLCareerPassingYards, over 21,000]
-
A.
careerTotalTouchdowns
Indicates the total number of touchdowns an entity has scored over the entire duration of its career.
-
B.
careerReceivingYards
Indicates the total number of yards a player has gained by receiving the ball over the course of their entire career.
-
C.
careerRushingTouchdowns
Indicates the total number of rushing touchdowns a player has scored over the entire span of their career.
-
D.
passingTouchdownsCareer
Indicates the total number of touchdown passes a player has thrown over the course of their entire 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_69c687eca814819095abb52316b1af80 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6e96f99088190a085476bcfca26fd |
completed | March 27, 2026, 8:32 p.m. |
| PD | Predicate disambiguation | batch_69c6e75f84e481909e7866186ae80cff |
completed | March 27, 2026, 8:23 p.m. |
Created at: March 27, 2026, 2:53 p.m.