Triple
T5646455
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | David Garrard |
E124395
|
entity |
| Predicate | careerNFLInterceptions |
P20504
|
FINISHED |
| Object | 54 interceptions (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: 54 interceptions (regular season) | Statement: [David Garrard, careerNFLInterceptions, 54 interceptions (regular season)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: careerNFLInterceptions Context triple: [David Garrard, careerNFLInterceptions, 54 interceptions (regular season)]
-
A.
interceptionsInNFL
chosen
Indicates the number of passes a player has intercepted while playing in the NFL.
-
B.
sportNumberOfReceptionsNFL
Indicates the number of receptions a player has made in NFL games.
-
C.
careerReceivingTouchdowns
Indicates the total number of touchdowns a player has scored by receiving the ball over the course of their entire career.
-
D.
careerFumbleRecoveries
Indicates the total number of times an entity has recovered a fumble over the course of their entire career.
-
E.
careerReceivingYards
Indicates the total number of yards a player has gained by receiving the ball over the course of their entire career.
- 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_69c00825df388190a58742fa9b1aa33d |
completed | March 22, 2026, 3:17 p.m. |
| NER | Named-entity recognition | batch_69c022abf0108190b10b3a9fe1688bf9 |
completed | March 22, 2026, 5:11 p.m. |
| PD | Predicate disambiguation | batch_69c01b2168508190b64b355cf50034ad |
completed | March 22, 2026, 4:38 p.m. |
Created at: March 22, 2026, 3:41 p.m.