Triple
T16165520
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bart Andrus |
E392292
|
entity |
| Predicate | hasWorkedAsCoachAtLevel |
P64064
|
FINISHED |
| Object | professional football |
—
|
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: professional football | Statement: [Bart Andrus, hasWorkedAsCoachAtLevel, professional football]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWorkedAsCoachAtLevel Context triple: [Bart Andrus, hasWorkedAsCoachAtLevel, professional football]
-
A.
hasCoachedFor
Indicates that one entity has served in a coaching role for another entity, such as a team, organization, or individual.
-
B.
hasCoachedCompetition
Indicates that one entity has served as a coach for another entity in the context of a specific competition or contest.
-
C.
hasCoachingExperienceAs
Indicates that an entity has experience providing coaching in a specified role or capacity.
-
D.
hasCoachedProfessionalSports
chosen
Indicates that a person has served in a coaching role for a professional-level sports team or athlete.
-
E.
hasCoachedDiscipline
Indicates that a person has coached or provided training in a particular discipline, field, or area of expertise.
- 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_69d87f1d32208190942e4e499a80c18c |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e21eb2a25c819095437b25e6ab83f3 |
completed | April 17, 2026, 11:51 a.m. |
| PD | Predicate disambiguation | batch_69e1828abb608190a99d86bce1d77de2 |
completed | April 17, 2026, 12:44 a.m. |
Created at: April 10, 2026, 5:02 a.m.