Triple
T2255995
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Romeo Crennel |
E49724
|
entity |
| Predicate | SuperBowlTitlesAsAssistantCoach |
P7112
|
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: [Romeo Crennel, SuperBowlTitlesAsAssistantCoach, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: SuperBowlTitlesAsAssistantCoach Context triple: [Romeo Crennel, SuperBowlTitlesAsAssistantCoach, 5]
-
A.
numberOfSuperBowlsWonAsAssistantCoach
chosen
Indicates the total count of Super Bowls that an individual has won specifically while serving in the role of an assistant coach.
-
B.
wonSuperBowlAsAssistantCoachWith
Indicates that one entity served as an assistant coach for a team that won the Super Bowl, in association with the other entity.
-
C.
SuperBowlTitlesAsHeadCoach
Indicates the number of Super Bowl titles an individual has won specifically in the role of head coach.
-
D.
hasCoachedInSuperBowl
Indicates that a person has served as a coach in at least one Super Bowl game.
-
E.
SuperBowlAppearanceAsHeadCoach
Indicates that a person, in their role as a head coach, led a team that participated in a Super Bowl game.
- 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_69a88aaa9250819095e127d0d77e8a32 |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc1559ff481908efe3f214b2570dc |
completed | March 7, 2026, 6:10 a.m. |
| PD | Predicate disambiguation | batch_69abbdb34c148190b51e99f540f97204 |
completed | March 7, 2026, 5:54 a.m. |
Created at: March 4, 2026, 7:47 p.m.