Triple
T419966
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Riggins |
E8078
|
entity |
| Predicate | careerRushingTouchdowns |
P14716
|
FINISHED |
| Object | 104 |
—
|
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: 104 | Statement: [John Riggins, careerRushingTouchdowns, 104]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: careerRushingTouchdowns Context triple: [John Riggins, careerRushingTouchdowns, 104]
-
A.
careerReceivingTouchdowns
Indicates the total number of touchdowns a player has scored by receiving the ball over the course of their entire career.
-
B.
careerReceivingYards
Indicates the total number of yards a player has gained by receiving the ball over the course of their entire career.
-
C.
careerReceptions
Indicates the total number of receptions a player has made over the course of their entire career.
-
D.
joinedNFL
Indicates that an entity became a member of, or started playing in, the National Football League (NFL).
-
E.
careerRunsScored
Indicates the total number of runs an entity has scored over the entire duration of their playing 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_69a2e7f1d1bc81909cf2dc9754a3c334 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2eebde1d881908fb212bfba9d7c67 |
completed | Feb. 28, 2026, 1:33 p.m. |
| PD | Predicate disambiguation | batch_69a2edd3b948819097d96c73d0a0f699 |
completed | Feb. 28, 2026, 1:29 p.m. |
| PDg | Predicate description generation | batch_69a2eeb8545c8190a2b8517e7ed5b92e |
completed | Feb. 28, 2026, 1:33 p.m. |
Created at: Feb. 28, 2026, 1:11 p.m.