Triple
T3158373
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kathy Selden |
E66043
|
entity |
| Predicate | professionInStory |
P34569
|
FINISHED |
| Object | chorus girl |
—
|
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: chorus girl | Statement: [Kathy Selden, professionInStory, chorus girl]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: professionInStory Context triple: [Kathy Selden, professionInStory, chorus girl]
-
A.
portraysProfession
Indicates that one entity depicts or represents another entity in a specific profession or occupational role.
-
B.
fictionalOccupation
chosen
Indicates that one entity is the imaginary or narrative-based job, role, or profession attributed to another entity within a fictional context.
-
C.
subjectOccupation
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
-
D.
featuresProtagonistOccupation
Indicates that the work’s main character has a specified occupation or job role.
-
E.
memberProfession
Indicates that a member or individual holds or practices a particular profession or occupation.
- 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_69ad85850c1481908a9e9c6242238de2 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada5ec0da88190a83f3448ac76e98e |
completed | March 8, 2026, 4:38 p.m. |
| PD | Predicate disambiguation | batch_69ad9dfbf0348190952a6bca8fc5fed1 |
completed | March 8, 2026, 4:04 p.m. |
Created at: March 8, 2026, 3:05 p.m.