Triple
T9370013
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brian Power |
E225504
|
entity |
| Predicate | usedInProfessionalField |
P85096
|
FINISHED |
| Object | sports officiating |
—
|
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: sports officiating | Statement: [Brian Power, usedInProfessionalField, sports officiating]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedInProfessionalField Context triple: [Brian Power, usedInProfessionalField, sports officiating]
-
A.
practicedInField
chosen
Indicates that an entity has engaged in practical work, training, or professional activity within a specified field or domain.
-
B.
isCommonInProfession
Indicates that something frequently occurs, appears, or is typical within a given profession or occupational field.
-
C.
usedInWork
Indicates that something (such as a concept, method, material, or component) is employed or applied within a particular work, project, or creation.
-
D.
hasProfessionalStatus
Indicates that an entity holds a particular professional standing, rank, or qualification within a field or occupation.
-
E.
basedOnProfession
Indicates that the relationship or action is determined or derived from a person’s profession or occupational role.
- 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_69ca842cbddc819099d71ecec48cf9e5 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd508274d88190a64b79ab731ac6a1 |
completed | April 1, 2026, 5:06 p.m. |
| PD | Predicate disambiguation | batch_69cc7a6abb8c81908c7a2f4ee92cc949 |
completed | April 1, 2026, 1:52 a.m. |
Created at: March 30, 2026, 7:43 p.m.