Triple
T16037474
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tony Gonzalez |
E389004
|
entity |
| Predicate | nflReceivingYardsCareer |
P10746
|
FINISHED |
| Object | 15127 |
—
|
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: 15127 | Statement: [Tony Gonzalez, nflReceivingYardsCareer, 15127]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nflReceivingYardsCareer Context triple: [Tony Gonzalez, nflReceivingYardsCareer, 15127]
-
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.
ledNFLInReceptions
Indicates that the subject had the highest number of pass receptions in the NFL over a specified season or time period.
-
C.
careerPassingYardsNFL
Indicates the total number of passing yards a player has accumulated over their entire career in the NFL.
-
D.
careerReceivingTouchdowns
Indicates the total number of touchdowns a player has scored by receiving the ball over the course of their entire career.
-
E.
NFLAllTimeRushingYardsLeader
Indicates that the subject is the player who has accumulated the most career rushing yards in NFL history.
- 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_69d86dada3808190825d5f80d72fbe88 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1ff63edb0819092cbb671967bbdcd |
completed | April 17, 2026, 9:37 a.m. |
| PD | Predicate disambiguation | batch_69e1826f34c081908005bb736f1c485d |
completed | April 17, 2026, 12:44 a.m. |
Created at: April 10, 2026, 4:56 a.m.