Triple
T11244199
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nikki King |
E266157
|
entity |
| Predicate | usedInProfession |
P65301
|
FINISHED |
| Object | record production |
—
|
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: record production | Statement: [Nikki King, usedInProfession, record production]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedInProfession Context triple: [Nikki King, usedInProfession, record production]
-
A.
usedInWork
Indicates that something (such as a concept, method, material, or component) is employed or applied within a particular work, project, or creation.
-
B.
usedByOccupation
chosen
Indicates that something (such as a tool, method, or resource) is utilized in the performance of a particular occupation or job.
-
C.
isCommonInProfession
Indicates that something frequently occurs, appears, or is typical within a given profession or occupational field.
-
D.
basedOnProfession
Indicates that the relationship or action is determined or derived from a person’s profession or occupational role.
-
E.
includesProfession
Indicates that one entity’s set of attributes, roles, or members contains a specific profession as part of it.
- 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_69d6aac656d48190b275efaa7d6074ee |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e91b0b808190bc38008bb344d180 |
completed | April 9, 2026, 5:59 p.m. |
| PD | Predicate disambiguation | batch_69d7878906f48190b63ddc103a0c8f9b |
completed | April 9, 2026, 11:03 a.m. |
Created at: April 8, 2026, 9:30 p.m.