Triple
T18951825
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Emmitt Thomas |
E463672
|
entity |
| Predicate | touchdownsOnInterceptions |
P41546
|
FINISHED |
| Object | 5 |
—
|
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: 5 | Statement: [Emmitt Thomas, touchdownsOnInterceptions, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: touchdownsOnInterceptions Context triple: [Emmitt Thomas, touchdownsOnInterceptions, 5]
-
A.
interceptionReturnTouchdowns
chosen
Indicates the number of times a defensive player returns an intercepted pass into the opponent’s end zone for a touchdown.
-
B.
touchdownsScored
Indicates the number of touchdowns that an entity has scored.
-
C.
RavensDefensiveTouchdowns
Indicates the number of touchdowns scored by the Ravens’ defense, typically by returning interceptions, fumbles, or blocked kicks for a score.
-
D.
careerTotalTouchdowns
Indicates the total number of touchdowns an entity has scored over the entire duration of its career.
-
E.
interceptionsInNFL
Indicates the number of passes a player has intercepted while playing 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_69d8dcffc278819086792a4ebfddfafa |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d54385e08190903a054681352d11 |
completed | April 20, 2026, 7:26 a.m. |
| PD | Predicate disambiguation | batch_69e4a2efec5c8190840704016bf547a1 |
completed | April 19, 2026, 9:40 a.m. |
Created at: April 10, 2026, noon