Triple
T2823487
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Riggo |
E54863
|
entity |
| Predicate | rushingTouchdownsInNFL |
P14716
|
FINISHED |
| Object | 100-plus |
—
|
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: 100-plus | Statement: [Riggo, rushingTouchdownsInNFL, 100-plus]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rushingTouchdownsInNFL Context triple: [Riggo, rushingTouchdownsInNFL, 100-plus]
-
A.
nflRushingTouchdownsLeader
Indicates the player who led all others in the number of rushing touchdowns in a given NFL season or context.
-
B.
careerRushingTouchdowns
chosen
Indicates the total number of rushing touchdowns a player has scored over the entire span of their career.
-
C.
ledNFLInPassingTouchdowns
Indicates that the subject was the league leader in passing touchdowns in the NFL for a given season or time period.
-
D.
touchdownsScored
Indicates the number of touchdowns that an entity has scored.
-
E.
careerReceivingTouchdowns
Indicates the total number of touchdowns a player has scored 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_69ab49e100c0819082a40cb797383243 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abdf15b7288190a03d1193cc0544a6 |
completed | March 7, 2026, 8:17 a.m. |
| PD | Predicate disambiguation | batch_69abdd08f2f481908c3da8a9c7a00552 |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 9:59 p.m.