Triple
T31804801
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mike Elko |
E811842
|
entity |
| Predicate | coachingRoleSpecialization |
P13768
|
FINISHED |
| Object | defense |
—
|
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: defense | Statement: [Mike Elko, coachingRoleSpecialization, defense]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: coachingRoleSpecialization Context triple: [Mike Elko, coachingRoleSpecialization, defense]
-
A.
coachingSpecialty
chosen
Indicates that a coach focuses on or is specialized in a particular area, topic, or type of coaching.
-
B.
coachedRole
Indicates that one entity served as a coach for another entity in a specific role or position.
-
C.
coachingTrait
Indicates that one entity possesses a characteristic, style, or quality specifically related to coaching.
-
D.
coachingAt
Indicates that one entity is serving as a coach at, or providing coaching services within, a particular organization, institution, or location.
-
E.
positionSpecialization
Indicates that one position is a more specialized or focused variant of another, broader position.
- 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_69f348e70d188190b4637c5509f81274 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6b967d5308190bbb66d0a8dd52612 |
completed | May 3, 2026, 2:56 a.m. |
| PD | Predicate disambiguation | batch_69f6b6293188819080d5041ca0adb969 |
completed | May 3, 2026, 2:42 a.m. |
Created at: April 30, 2026, 11:42 p.m.