Triple
T7009026
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marcus Allen |
E162531
|
entity |
| Predicate | receivingYardsInNFL |
P10746
|
FINISHED |
| Object | 5411 |
—
|
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: 5411 | Statement: [Marcus Allen, receivingYardsInNFL, 5411]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: receivingYardsInNFL Context triple: [Marcus Allen, receivingYardsInNFL, 5411]
-
A.
careerReceivingYards
chosen
Indicates the total number of yards a player has gained by receiving the ball over the course of their entire career.
-
B.
sportNumberOfReceptionsNFL
Indicates the number of receptions a player has made in NFL games.
-
C.
ledLeagueInReceivingYards
Indicates that the subject had the highest total receiving yards in the league for a given season or time period.
-
D.
ledNFLInPassingYards
Indicates that the subject was the league leader in total passing yards in the NFL for a given season.
-
E.
careerReceivingTouchdowns
Indicates the total number of touchdowns a player has scored by receiving 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_69c6885928148190ae31909fbb5e9849 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6dc38039c8190ab420d62bb11b4d9 |
completed | March 27, 2026, 7:36 p.m. |
| PD | Predicate disambiguation | batch_69c6d7c790288190b7cbbaa4a5f9c91d |
completed | March 27, 2026, 7:17 p.m. |
Created at: March 27, 2026, 2:33 p.m.