Triple
T32256127
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Margo Durrell |
E824026
|
entity |
| Predicate | basedOnOccupationOfRealPerson |
P71047
|
FINISHED |
| Object | author |
—
|
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: author | Statement: [Margo Durrell, basedOnOccupationOfRealPerson, author]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: basedOnOccupationOfRealPerson Context triple: [Margo Durrell, basedOnOccupationOfRealPerson, author]
-
A.
hasOccupationInReality
Indicates that an entity holds or performs a specific occupation in the real world, as opposed to fictional or hypothetical contexts.
-
B.
basedOnRealPersonFor
Indicates that one entity is created, modeled, or inspired using a specific real person as its basis.
-
C.
basedOnProfession
chosen
Indicates that the relationship or action is determined or derived from a person’s profession or occupational role.
-
D.
basedOnCareerOf
Indicates that something (such as a work, character, or storyline) is derived from, inspired by, or modeled on the career or professional life of a particular person.
-
E.
basedOnCharacterOccupation
Indicates that something is derived from, inspired by, or determined according to a character’s occupation or job 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_69f3490db0748190bfef6e50c95d39d3 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69fe629b4fa481908467c7c41b77f0c6 |
completed | May 8, 2026, 10:24 p.m. |
| PD | Predicate disambiguation | batch_69fe61bb260c819083f9378a3a06ca47 |
completed | May 8, 2026, 10:20 p.m. |
Created at: May 1, 2026, 12:41 a.m.