Triple
T8463809
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Julio Jones |
E200107
|
entity |
| Predicate | ledNFLInReceivingYards |
P69375
|
FINISHED |
| Object | 2015 |
—
|
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: 2015 | Statement: [Julio Jones, ledNFLInReceivingYards, 2015]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ledNFLInReceivingYards Context triple: [Julio Jones, ledNFLInReceivingYards, 2015]
-
A.
ledLeagueInReceivingYards
chosen
Indicates that the subject had the highest total receiving yards in the league for a given season or time period.
-
B.
ledNFLInPassingYards
Indicates that the subject was the league leader in total passing yards in the NFL for a given season.
-
C.
sportNumberOfReceptionsNFL
Indicates the number of receptions a player has made in NFL games.
-
D.
ledNFLInPassingTouchdowns
Indicates that the subject was the league leader in passing touchdowns in the NFL for a given season or time period.
-
E.
careerReceivingYards
Indicates the total number of yards a player has gained 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_69ca83198c4c8190a337bf717d1813f5 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe4a39bd48190b72be7e03cff323b |
completed | March 31, 2026, 3:13 p.m. |
| PD | Predicate disambiguation | batch_69cbd10072cc819084be1ed9ac7ebe9d |
completed | March 31, 2026, 1:49 p.m. |
Created at: March 30, 2026, 6:10 p.m.