Triple
T11036785
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jonathan Creek |
E260905
|
entity |
| Predicate | mainCharacterProfessionInSeries |
P21567
|
FINISHED |
| Object | designer of magic tricks |
—
|
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: designer of magic tricks | Statement: [Jonathan Creek, mainCharacterProfessionInSeries, designer of magic tricks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainCharacterProfessionInSeries Context triple: [Jonathan Creek, mainCharacterProfessionInSeries, designer of magic tricks]
-
A.
featuresProtagonistOccupation
chosen
Indicates that the work’s main character has a specified occupation or job role.
-
B.
narrativeRoleInSeries
Indicates the specific narrative function or role an entity plays within a particular series or serialized work.
-
C.
followsCharacterOccupation
Indicates that one character’s occupation or job role comes after or succeeds another character’s occupation in a sequence or progression.
-
D.
starOccupationInSeries
Indicates that an individual has a specific occupation or role as a starring character within a particular series.
-
E.
literarySeriesCharacter
Indicates that a character appears in, is part of, or is associated with a particular literary series.
- 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_69d6aa979bdc8190bf0e79104cc098c1 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d797e9e3fc8190802195ac9fcb8e28 |
completed | April 9, 2026, 12:13 p.m. |
| PD | Predicate disambiguation | batch_69d74407cb088190ba37c8da3d342b64 |
completed | April 9, 2026, 6:15 a.m. |
Created at: April 8, 2026, 9:25 p.m.