Triple
T32011085
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harvey Cheyne Jr. |
E817402
|
entity |
| Predicate | learnsOccupationSkills |
P149666
|
FINISHED |
| Object | fishing |
—
|
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: fishing | Statement: [Harvey Cheyne Jr., learnsOccupationSkills, fishing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: learnsOccupationSkills Context triple: [Harvey Cheyne Jr., learnsOccupationSkills, fishing]
-
A.
apprenticeshipOccupation
chosen
Indicates that one entity serves as the occupation or trade in which another entity is undergoing or has undergone apprenticeship training.
-
B.
killsAsPartOfJob
Indicates that one entity kills another as a regular or expected duty within their professional role or occupation.
-
C.
skilledIn
Indicates that an entity possesses ability, expertise, or proficiency in performing or using another entity (such as a task, tool, or domain).
-
D.
subjectOccupation
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
-
E.
derivesFromOccupation
Indicates that one entity originates from, is obtained through, or is a result of another entity’s occupation or professional 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_69f348f9e5d081908cc3f57c4942af52 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6b42f7b5081908dae0678c4cd6888 |
completed | May 3, 2026, 2:34 a.m. |
| PD | Predicate disambiguation | batch_69f6b151ad008190836c1bcdec503ce2 |
completed | May 3, 2026, 2:22 a.m. |
Created at: May 1, 2026, 12:15 a.m.