Triple
T30885354
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | State of Mind |
E786745
|
entity |
| Predicate | featuresCharacterWithOccupation |
P56368
|
FINISHED |
| Object | therapist |
—
|
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: therapist | Statement: [State of Mind, featuresCharacterWithOccupation, therapist]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresCharacterWithOccupation Context triple: [State of Mind, featuresCharacterWithOccupation, therapist]
-
A.
featuresProtagonistOccupation
Indicates that the work’s main character has a specified occupation or job role.
-
B.
followsCharacterOccupation
Indicates that one character’s occupation or job role comes after or succeeds another character’s occupation in a sequence or progression.
-
C.
settingOfCharacterOccupation
Indicates the place or environment in which a character performs or holds their occupation.
-
D.
workCharacter
Indicates that a person is a fictional or narrative character appearing in a particular creative work.
-
E.
notableCharacterOccupation
chosen
Indicates that a notable character is associated with a specific occupation or professional 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_69f224bbfa7c81908448e0c261c523e3 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_6a00372ff0e48190b3ed91f9bae9da6c |
completed | May 10, 2026, 7:43 a.m. |
| PD | Predicate disambiguation | batch_6a00359c1b8481909c1e43df9f5a789a |
completed | May 10, 2026, 7:37 a.m. |
Created at: April 29, 2026, 8:49 p.m.