Triple
T36200008
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | This Is Not a Pity Memoir |
E1047233
|
entity |
| Predicate | relatedProfessionOfAuthor |
P204787
|
FINISHED |
| Object | screenwriter |
—
|
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: screenwriter | Statement: [This Is Not a Pity Memoir, relatedProfessionOfAuthor, screenwriter]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relatedProfessionOfAuthor Context triple: [This Is Not a Pity Memoir, relatedProfessionOfAuthor, screenwriter]
-
A.
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.
-
B.
coAuthorOccupation
Indicates that two or more co-authors share the same or closely related professional occupation.
-
C.
relatedWorkOfPerson
Indicates that a work (such as a publication, project, or creation) is associated with or produced by a particular person.
-
D.
associatedProfessionOfFounder
Indicates the professional field or occupation linked to the founder of an entity.
-
E.
publisherProfessionOfAuthor
Indicates that the profession specified is the occupation or professional role of the author associated with a given publisher.
- F. None of above. chosen
Provenance (4 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_69f76e414bdc8190996f15a544220a3d |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037c92f03c8190ae2751270b195423 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0a54cc8190868c1bfa1590d1a6 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c82f8c88190bd77a086023ac0e1 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:08 p.m.