Triple
T838303
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Troy Aikman |
E18119
|
entity |
| Predicate | interceptionsInNFL |
P20504
|
FINISHED |
| Object | 141 |
—
|
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: 141 | Statement: [Troy Aikman, interceptionsInNFL, 141]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: interceptionsInNFL Context triple: [Troy Aikman, interceptionsInNFL, 141]
-
A.
interceptions
Indicates that one entity successfully stops, seizes, or cuts off another entity or action in progress, preventing it from reaching its intended target or outcome.
-
B.
interceptedQuarterback
Indicates that a defensive player successfully caught a pass thrown by the quarterback, resulting in an interception.
-
C.
interceptionYardLine
Indicates the yard line on the field where an interception occurs during a play.
-
D.
ledNFLInPassingTouchdowns
Indicates that the subject was the league leader in passing touchdowns in the NFL for a given season or time period.
-
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. chosen
Provenance (4 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_69a49389f44881909a608fb27d89f247 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4abd0e8bc8190afe29cd4745c2f86 |
completed | March 1, 2026, 9:12 p.m. |
| PD | Predicate disambiguation | batch_69a4aa7dfc5c8190890c9df485d73a86 |
completed | March 1, 2026, 9:07 p.m. |
| PDg | Predicate description generation | batch_69a4ab4893e481908632102d240466dc |
completed | March 1, 2026, 9:10 p.m. |
Created at: March 1, 2026, 7:38 p.m.