Triple
T36842055
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kenyan Drake |
E910432
|
entity |
| Predicate | rushingTouchdownsNFL |
P70224
|
FINISHED |
| Object | over 30 |
—
|
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: over 30 | Statement: [Kenyan Drake, rushingTouchdownsNFL, over 30]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rushingTouchdownsNFL Context triple: [Kenyan Drake, rushingTouchdownsNFL, over 30]
-
A.
hasNFLRushingTouchdowns
Indicates that an entity (typically a player or team) has scored one or more rushing touchdowns in NFL games.
-
B.
nflRushingTouchdownsLeader
Indicates the player who led all others in the number of rushing touchdowns in a given NFL season or context.
-
C.
rushingTouchdownsInSeason
chosen
Indicates the number of rushing touchdowns a player scores during a single season.
-
D.
returnTouchdownsInNFL
Indicates the number of touchdowns a player scores by returning the ball (e.g., on kickoffs, punts, interceptions, or fumbles) in NFL games.
-
E.
touchdownsScored
Indicates the number of touchdowns that an entity has scored.
- 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_69f76e7f65a881908651b702da592b6d |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fa0a7b00948190a257273d9968c5d7 |
completed | May 5, 2026, 3:19 p.m. |
| PD | Predicate disambiguation | batch_69f9fec9c9488190ae2a349651a02782 |
completed | May 5, 2026, 2:29 p.m. |
Created at: May 3, 2026, 4:13 p.m.