Triple
T5646454
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | David Garrard |
E124395
|
entity |
| Predicate | careerNFLTouchdowns |
P65401
|
FINISHED |
| Object | 89 passing touchdowns (regular season) |
—
|
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: 89 passing touchdowns (regular season) | Statement: [David Garrard, careerNFLTouchdowns, 89 passing touchdowns (regular season)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: careerNFLTouchdowns Context triple: [David Garrard, careerNFLTouchdowns, 89 passing touchdowns (regular season)]
-
A.
careerReceivingTouchdowns
Indicates the total number of touchdowns a player has scored by receiving the ball over the course of their entire career.
-
B.
careerRushingTouchdowns
Indicates the total number of rushing touchdowns a player has scored over the entire span of their career.
-
C.
careerTotalTouchdowns
Indicates the total number of touchdowns an entity has scored over the entire duration of its career.
-
D.
ledNFLInPassingTouchdowns
Indicates that the subject was the league leader in passing touchdowns in the NFL for a given season or time period.
-
E.
gameWinningTouchdownPasser
Indicates that the subject is the player who threw the touchdown pass that secured the victory in the game for the subject's team.
- 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_69c00825df388190a58742fa9b1aa33d |
completed | March 22, 2026, 3:17 p.m. |
| NER | Named-entity recognition | batch_69c022aa650c819088d9046b82fab631 |
completed | March 22, 2026, 5:11 p.m. |
| PD | Predicate disambiguation | batch_69c01b2168508190b64b355cf50034ad |
completed | March 22, 2026, 4:38 p.m. |
| PDg | Predicate description generation | batch_69c01f0727bc8190b16e9a669c04b4dd |
completed | March 22, 2026, 4:55 p.m. |
Created at: March 22, 2026, 3:41 p.m.