Triple
T6432536
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Doctors' Plot |
E129811
|
entity |
| Predicate | targetedProfession |
P69514
|
FINISHED |
| Object | medical doctors |
—
|
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: medical doctors | Statement: [Doctors' Plot, targetedProfession, medical doctors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: targetedProfession Context triple: [Doctors' Plot, targetedProfession, medical doctors]
-
A.
targetCareer
Indicates that one entity is the intended or pursued career or professional goal of another entity.
-
B.
includesProfession
chosen
Indicates that one entity’s set of attributes, roles, or members contains a specific profession as part of it.
-
C.
memberProfession
Indicates that a member or individual holds or practices a particular profession or occupation.
-
D.
careerType
Indicates the kind or category of professional occupation or career path associated with an entity.
-
E.
subjectOccupation
Indicates that the subject holds or performs a particular job, profession, or role as their 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_69c0084caac48190a7bc2ad8ba44536f |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0693de6ac81909f3e330363a52102 |
completed | March 22, 2026, 10:12 p.m. |
| PD | Predicate disambiguation | batch_69c060f96980819091bab9335922a457 |
completed | March 22, 2026, 9:36 p.m. |
Created at: March 22, 2026, 4:44 p.m.