Triple
T20478587
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. Mumford |
E502390
|
entity |
| Predicate | primaryProfessionInStory |
P42552
|
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: [Dr. Mumford, primaryProfessionInStory, therapist]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryProfessionInStory Context triple: [Dr. Mumford, primaryProfessionInStory, therapist]
-
A.
fictionalOccupation
Indicates that one entity is the imaginary or narrative-based job, role, or profession attributed to another entity within a fictional context.
-
B.
roleInStories
chosen
Indicates the specific function, position, or character part an entity plays within one or more stories.
-
C.
featuresProtagonistOccupation
Indicates that the work’s main character has a specified occupation or job role.
-
D.
portraysProfession
Indicates that one entity depicts or represents another entity in a specific profession or occupational role.
-
E.
fictionalProfessionSpecialty
Indicates that a fictional character’s professional role is specialized in a particular subfield, focus area, or niche within that profession.
- 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_69e0b4af32848190aea80682b44d5d6e |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e69b54c8188190a71e35fab8d194a6 |
completed | April 20, 2026, 9:32 p.m. |
| PD | Predicate disambiguation | batch_69e5768372988190b08ef8ae67d42ab6 |
completed | April 20, 2026, 12:42 a.m. |
Created at: April 16, 2026, 11:34 a.m.