Triple
T25035154
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CraigyFerg |
E626955
|
entity |
| Predicate | relatedToProfessionOfOwner |
P35215
|
FINISHED |
| Object | comedy |
—
|
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: comedy | Statement: [CraigyFerg, relatedToProfessionOfOwner, comedy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relatedToProfessionOfOwner Context triple: [CraigyFerg, relatedToProfessionOfOwner, comedy]
-
A.
isAssociatedWithProfessionOfBearer
chosen
Indicates that one entity is connected to, or involved with, the profession or occupational role held by another entity.
-
B.
hasProfessionalRelationshipWith
Indicates a formal, work-related connection or collaboration exists between the two entities in a professional context.
-
C.
partnerInProfession
Indicates that two or more entities share a professional partnership or collaborate together within the same occupation or field.
-
D.
relatedProfession
Indicates that two entities have professions that are connected or associated in some meaningful way, such as being in the same field, industry, or professional domain.
-
E.
ownerOccupation
Indicates that the occupation or job role of an entity that owns something is being specified or described.
- 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_69e2ff2a2c088190be513727ee8bfe78 |
completed | April 18, 2026, 3:48 a.m. |
| NER | Named-entity recognition | batch_69f6afebd7ec8190ab696f363d84abf0 |
completed | May 3, 2026, 2:16 a.m. |
| PD | Predicate disambiguation | batch_69f6aca204148190850a3dc325bc07b7 |
completed | May 3, 2026, 2:02 a.m. |
Created at: April 18, 2026, 6:08 a.m.