Triple
T21524318
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Secret Lives of Dentists |
E531054
|
entity |
| Predicate | occupationOfMainCharacters |
P21567
|
FINISHED |
| Object | dentist |
—
|
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: dentist | Statement: [The Secret Lives of Dentists, occupationOfMainCharacters, dentist]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: occupationOfMainCharacters Context triple: [The Secret Lives of Dentists, occupationOfMainCharacters, dentist]
-
A.
notableCharacterOccupation
Indicates that a notable character is associated with a specific occupation or professional role.
-
B.
featuresProtagonistOccupation
chosen
Indicates that the work’s main character has a specified occupation or job role.
-
C.
spouseOccupationInSeries
Indicates that a character’s spouse has a particular occupation within the context of a series.
-
D.
followsCharacterOccupation
Indicates that one character’s occupation or job role comes after or succeeds another character’s occupation in a sequence or progression.
-
E.
mainCharactersAre
Indicates that the specified entities serve as the primary or central characters in a narrative or work.
- 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_69e0c45d95a081908e7962ad215da746 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ee884f4504819086bd632e62f02f58 |
completed | April 26, 2026, 9:49 p.m. |
| PD | Predicate disambiguation | batch_69e6320043bc81909417c41a718652ba |
completed | April 20, 2026, 2:02 p.m. |
Created at: April 16, 2026, 6:26 p.m.