Triple
T16041893
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brian Finneran |
E389116
|
entity |
| Predicate | NFLReceivingYards |
P10746
|
FINISHED |
| Object | 3234 |
—
|
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: 3234 | Statement: [Brian Finneran, NFLReceivingYards, 3234]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: NFLReceivingYards Context triple: [Brian Finneran, NFLReceivingYards, 3234]
-
A.
ledNFLInReceptions
Indicates that the subject had the highest number of pass receptions in the NFL over a specified season or time period.
-
B.
careerReceivingYards
chosen
Indicates the total number of yards a player has gained by receiving the ball over the course of their entire career.
-
C.
sportNumberOfReceptionsNFL
Indicates the number of receptions a player has made in NFL games.
-
D.
ledLeagueInReceivingYards
Indicates that the subject had the highest total receiving yards in the league for a given season or time period.
-
E.
careerPassingYardsNFL
Indicates the total number of passing yards a player has accumulated over their entire career in the NFL.
- 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.