Triple
T30394940
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Graduel Christopher Darin Carter |
E773189
|
entity |
| Predicate | statisticCareerReceptions |
P54272
|
FINISHED |
| Object | over 1100 |
—
|
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 1100 | Statement: [Graduel Christopher Darin Carter, statisticCareerReceptions, over 1100]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: statisticCareerReceptions Context triple: [Graduel Christopher Darin Carter, statisticCareerReceptions, over 1100]
-
A.
careerReceivingYards
Indicates the total number of yards a player has gained by receiving the ball over the course of their entire career.
-
B.
sportNumberOfReceptionsNFL
chosen
Indicates the number of receptions a player has made in NFL games.
-
C.
ledNFLInReceptions
Indicates that the subject had the highest number of pass receptions in the NFL over a specified season or time period.
-
D.
careerReceivingTouchdowns
Indicates the total number of touchdowns a player has scored by receiving the ball over the course of their entire career.
-
E.
touchdownReceptionLeaderForTeam
Indicates the player who has recorded the most touchdown receptions for a given team.
- 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_69f2248ef0a48190aa54d4d8ac3e5758 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f6953bafb88190a860e9c68a3dd4b2 |
completed | May 3, 2026, 12:22 a.m. |
| PD | Predicate disambiguation | batch_69f690ed5d008190831cf8e44cce28af |
completed | May 3, 2026, 12:03 a.m. |
Created at: April 29, 2026, 8:02 p.m.