Triple
T3478548
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marv Levy |
E73433
|
entity |
| Predicate | CFLGreyCupChampionshipsWonAsHeadCoach |
P36156
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Marv Levy, CFLGreyCupChampionshipsWonAsHeadCoach, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: CFLGreyCupChampionshipsWonAsHeadCoach Context triple: [Marv Levy, CFLGreyCupChampionshipsWonAsHeadCoach, 2]
-
A.
GreyCupWin
Indicates that a team has won the Canadian Football League championship game, the Grey Cup, in a given season or year.
-
B.
firstGreyCupWin
Indicates the event in which a team achieves its first-ever victory in the Grey Cup championship.
-
C.
AFCChampionCoach
Indicates that a coach led a team that won the American Football Conference (AFC) championship.
-
D.
numberOfGreyCupChampionships
chosen
Indicates the count of Grey Cup championship titles that an entity has won.
-
E.
totalStanleyCupsAsHeadCoach
Indicates the total number of Stanley Cup championships an individual has won in the role of head coach.
- 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_69ad85b3c9b08190857cae74c7f36da9 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adbb5ca73c81908256e3339a3a6f9f |
completed | March 8, 2026, 6:09 p.m. |
| PD | Predicate disambiguation | batch_69adae0935ac8190bfa8a8bd3dcd3301 |
completed | March 8, 2026, 5:12 p.m. |
Created at: March 8, 2026, 3:17 p.m.